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Bioinformatics Workshop 1 Sequences and Similarity Searches. Open a web browser and type in the URL: informatics.gurdon.cam.ac.uk/online/workshops Bookmark this page Click on the link to the file: useful-websites.html Bookmark this page too - PowerPoint PPT Presentation
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Bioinformatics Workshop 1Sequences and Similarity Searches
bull Open a web browser and type in the URLndash informaticsgurdoncamacukonlineworkshopsndash Bookmark this page
bull Click on the link to the filendash useful-websiteshtmlndash Bookmark this page toondash It also contains links to the example sequence
files used in the workshop and the presentations themselves
The Basic Questions
Where and how do I find something
How do I know itrsquos real
Exercise 0
Write a concise definition of what a gene is
Part 1 Structural Genomics
DNA arranged in chromosomes
Vertebrate ~ 109 base pairs
Chromosomes and Genes
Total of ~30000 genes on ~20 chromosomes
1000 ndash 2000 genes per chromosome
locus
Gene to Protein~ gene
mRNA
protein
genome
primary transcript
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
Sequence Signals
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
mRNA
MLTIL AL
Genomic Signals
transcription start site
===CGCTATAAGCG====================
===CGCAATAAAGCG===================
polyadenylation signal
===CACGATCGAGTC===================
promoters
enhancers
==ACGTAhelliphelliphelliphellipCAGTA====================
splice sites
Derivative Sequences
mRNA
capture by cloning into cDNA library
3rsquo EST
5rsquo EST
cDNA sequence
EST single pass sequence from each end of the clone
cDNA multiple pass sequencing over whole length of the clone
5rsquo 3rsquo
Gene Models
gene modelexons
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
The Basic Questions
Where and how do I find something
How do I know itrsquos real
Exercise 0
Write a concise definition of what a gene is
Part 1 Structural Genomics
DNA arranged in chromosomes
Vertebrate ~ 109 base pairs
Chromosomes and Genes
Total of ~30000 genes on ~20 chromosomes
1000 ndash 2000 genes per chromosome
locus
Gene to Protein~ gene
mRNA
protein
genome
primary transcript
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
Sequence Signals
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
mRNA
MLTIL AL
Genomic Signals
transcription start site
===CGCTATAAGCG====================
===CGCAATAAAGCG===================
polyadenylation signal
===CACGATCGAGTC===================
promoters
enhancers
==ACGTAhelliphelliphelliphellipCAGTA====================
splice sites
Derivative Sequences
mRNA
capture by cloning into cDNA library
3rsquo EST
5rsquo EST
cDNA sequence
EST single pass sequence from each end of the clone
cDNA multiple pass sequencing over whole length of the clone
5rsquo 3rsquo
Gene Models
gene modelexons
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Part 1 Structural Genomics
DNA arranged in chromosomes
Vertebrate ~ 109 base pairs
Chromosomes and Genes
Total of ~30000 genes on ~20 chromosomes
1000 ndash 2000 genes per chromosome
locus
Gene to Protein~ gene
mRNA
protein
genome
primary transcript
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
Sequence Signals
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
mRNA
MLTIL AL
Genomic Signals
transcription start site
===CGCTATAAGCG====================
===CGCAATAAAGCG===================
polyadenylation signal
===CACGATCGAGTC===================
promoters
enhancers
==ACGTAhelliphelliphelliphellipCAGTA====================
splice sites
Derivative Sequences
mRNA
capture by cloning into cDNA library
3rsquo EST
5rsquo EST
cDNA sequence
EST single pass sequence from each end of the clone
cDNA multiple pass sequencing over whole length of the clone
5rsquo 3rsquo
Gene Models
gene modelexons
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Chromosomes and Genes
Total of ~30000 genes on ~20 chromosomes
1000 ndash 2000 genes per chromosome
locus
Gene to Protein~ gene
mRNA
protein
genome
primary transcript
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
Sequence Signals
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
mRNA
MLTIL AL
Genomic Signals
transcription start site
===CGCTATAAGCG====================
===CGCAATAAAGCG===================
polyadenylation signal
===CACGATCGAGTC===================
promoters
enhancers
==ACGTAhelliphelliphelliphellipCAGTA====================
splice sites
Derivative Sequences
mRNA
capture by cloning into cDNA library
3rsquo EST
5rsquo EST
cDNA sequence
EST single pass sequence from each end of the clone
cDNA multiple pass sequencing over whole length of the clone
5rsquo 3rsquo
Gene Models
gene modelexons
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
locus
Gene to Protein~ gene
mRNA
protein
genome
primary transcript
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
Sequence Signals
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
mRNA
MLTIL AL
Genomic Signals
transcription start site
===CGCTATAAGCG====================
===CGCAATAAAGCG===================
polyadenylation signal
===CACGATCGAGTC===================
promoters
enhancers
==ACGTAhelliphelliphelliphellipCAGTA====================
splice sites
Derivative Sequences
mRNA
capture by cloning into cDNA library
3rsquo EST
5rsquo EST
cDNA sequence
EST single pass sequence from each end of the clone
cDNA multiple pass sequencing over whole length of the clone
5rsquo 3rsquo
Gene Models
gene modelexons
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
Sequence Signals
CTACCATCCATGCTAACCATTCTACTAGCATAACTGGCTA
mRNA
MLTIL AL
Genomic Signals
transcription start site
===CGCTATAAGCG====================
===CGCAATAAAGCG===================
polyadenylation signal
===CACGATCGAGTC===================
promoters
enhancers
==ACGTAhelliphelliphelliphellipCAGTA====================
splice sites
Derivative Sequences
mRNA
capture by cloning into cDNA library
3rsquo EST
5rsquo EST
cDNA sequence
EST single pass sequence from each end of the clone
cDNA multiple pass sequencing over whole length of the clone
5rsquo 3rsquo
Gene Models
gene modelexons
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Genomic Signals
transcription start site
===CGCTATAAGCG====================
===CGCAATAAAGCG===================
polyadenylation signal
===CACGATCGAGTC===================
promoters
enhancers
==ACGTAhelliphelliphelliphellipCAGTA====================
splice sites
Derivative Sequences
mRNA
capture by cloning into cDNA library
3rsquo EST
5rsquo EST
cDNA sequence
EST single pass sequence from each end of the clone
cDNA multiple pass sequencing over whole length of the clone
5rsquo 3rsquo
Gene Models
gene modelexons
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Derivative Sequences
mRNA
capture by cloning into cDNA library
3rsquo EST
5rsquo EST
cDNA sequence
EST single pass sequence from each end of the clone
cDNA multiple pass sequencing over whole length of the clone
5rsquo 3rsquo
Gene Models
gene modelexons
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Gene Models
gene modelexons
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Sequences and Genes(Accession Numbers and Names)
AAB229701
AAP212451
CAA415451
NP_1877592
proteins
S431051mRNAscDNAs lsquosimilar to Cyclin B1 [mus musculus]rsquo
gene
BT0064371 lsquoCyclin B1 isoform 1 [mus musculus]rsquo
X587081
NM_1119853 lsquoCCNB1 Cyclin B1 [mus musculus]rsquo
lsquoCyclin B1 isoform 2 [mus musculus]rsquo
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Gene Symbols Names Etc
Gene Symbol CCNB1
Gene Name cyclin B1 [Homo sapiens]
Description G2mitotic-specific cyclin B1
Aliases CCNB CYCB1
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
A Gene-Centric View
Entrez Genehttpwwwncbinlmnihgov
Cyclin B1
S431051
BT0064371
X587081
NM_1119853
AAB229701
AAP212451
CAA415451
NP_1877592
Exercise 1
Go to Entrez Gene and look for your favourite gene or genes
genomic location
expression data
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Sequences and Accession Numbers
NM_0010159221 gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC0096381 gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
NM_0010159222 gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAA
NP_0010159221 protein translated from mRNA
XM_0011025671 predicted mRNA
XP_0010897651 predicted protein translated from predicted mRNA
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
mRNA Splicing Signals
gene model
genome
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTAC CATTTTATACTCATGCAACGGACCGT AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAGdonor
TTTCAG acceptor
mRNA
exon intron exon intron exon
splice sites
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Gene PredictionsGiven- coding sequence must run from ATG ndash STOP codon in-frame- introns GT AG can be spliced out
Also take a statistical approach- coding and non-coding sequence are slightly different in composition- some lsquopossiblersquo splice sites are more likely than others
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
scan genomic sequence hellip
CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA
most likely gene model
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Supporting Evidence
EST evidence
genome
gene model
We note that in the absence of EST evidence it is only really possible to predict coding sequence with any confidence (and even thenhellip)
So predicted genes based on computational gene models alone will usually lack UTR regions which has some important consequences
exons 1 2 3 4
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
TheoreticalPredicted Sequences
genome
predicted gene modelexons 1 2 3 4
Wersquove now reversed the process of working out exon structure from aligning cDNA sequences against the genome sequence but we shouldnrsquot lose sight of the fact that we donrsquot really know if these predicted proteins exists ndash especially where supporting EST evidence is weak or non-existent
predicted transcript
predicted protein
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Sequences for a model organism
ESTs ndash millions pound10 eachCheap to sequence ndash so we get millions per organismBut lots of errorsAnd incomplete gene sequencesCan give us relative expression levels
cDNAs ndash tens of thousands pound1000 eachExpensive ndash but only need to do one (or a small number) per geneFew errors with multipass sequencingGives us protein sequences
Genomes ndash one pound30000000Extremely expensiveBut the only way to get the whole pictureGives us gene regulation
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
So Whatrsquos in the Databases Now
15000000ESTs
3300000cDNAs
NCBI July 2005
2700000proteins
950000proteins
nrRefSeq
DNA
Proteins
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Part 2 Comparative Genomics
ATGAAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGCCCTGATGCATGCCGCCAACGGATGTCCTG
Imagine one mutation gets fixed every 100000 years in this gene sequencehellip
Gene sequence
Evolution by sequence mutation
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Speciation
ATGAAGGCTGCCTACGACTGCCGTG
ATGCAGGCTGCCTACGACTGCCGTGATGCAGGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCGTGATGCATGCTGCCAACGACTGCCCTGATGCATGCTGCCAACGGCTGCCCTGATGCATGCTGCCAACGGATGCCCTG
Gene AATGAAGGCTGCCTACGACTGCCGTG
ATGAAGGCCGCCTACGACTGCCGTGATGAAGGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACTGTCGTGATGAAAGCCGCCAACGACAGTCGTGATGAAAGCCGCCTACGACAGTCGTGATGAAAGCCGCCTACGACAGTCCTG
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
If the genetic difference means they can no longer interbreed with fertile offspring ndash then we have a new specieshellip
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Residual Similarity
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
ATGCATGCTGCCAACGGATGCCCTG
ATGGAAGGCGCTTAGGATAGTCCAG||| | | || | | | || |
After longer periods of evolution homology may no longer be detectable in the DNA sequencehellip
We can still easily detect residual similarity between these sequences this is what we call homology ndash detectable similarity because of common evolutionary origin
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Computers Can Detect Homology
In fact computers are very good at this task ndash the two primary challenges are
(a) performing the search fast enough to look through millions of sequence in a timescale compatible with a lab scientistrsquos attention span
(b) at low levels of similarity being able to distinguish between biologically related sequences and chance matcheshellip
ATGCATGCTGCCAACGGATGCCCTG
ATGAAAGCCGCCTACGACAGTCCTG||| | || ||| ||| | ||||
GCTGACTCGTAGCGCTTAGCTAGCT
CCAACATCTAGCCAGATTAGTTAGT | || | | | |
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Orthologs
A A
A Gene duplication though speciation The two copies of Gene
A will now evolve independently but will continue to have the ~same function
They are ORTHOLOGS
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Paralogs
A
Gene duplication though internal genome duplication
The two copies of Gene A will now evolve independently but will probably not continue to have exactly the same function
They are PARALOGS
A
A Arsquo
A
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
lsquoOtherrsquo-logsWhat about gene duplication after speciation
How can we describe the relationship(s) between the various copies of gene A in the two frogs
Bear in mind that understanding gene function is more important than semanticshellip
The two copies of A in the orange frog are sometimes called IN-PARALOGS
If they were also present in the green frog (and therefore were in the ancestor species) they would be OUT-PARALOGS
A
A
A
Arsquo A
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
The Essential Paradigm
1 any group of modern species can be traced back to some extinct common ancestor
A
A
2 in all likelihood they share orthologous genes which have the same function in the modern animal as in the extinct ancestor
3 If we can experimentally determine the function of a gene in one of these organisms then there is a good chance the ORTHOLOGOUS gene in another organism will have the same function
A A
cyclin b1
cyclin b1
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Function Conserved Longer than Detectable Similarity
start from first self-replicating sequence
same function detectable similarity
living organisms
whole genome duplication local duplication
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Redundancy in the Genetic Code
GCA A alanine GCC A GCG A GCT A
TGC C cystine TGT C
GAC D aspartate GAT D
GGA G glycine GGC G GGG G GGT G
lsquoSynonymousrsquo or lsquosilentrsquo mutations in the third position of the codon triplets have no effect on the amino acid coded for ndash so there is no evolutionary pressure against thishellip
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Protein Similarity Persists Longer
CTATCACGAGAACCTGTGCTATCCCGAGAACCTGTGCTATCCCGAGAACCAGTGCTATCCCGTGAACCAGTGCTATCCCGTGAGCCAGTGCTATCCCGTGAGCCAGTTCTGTCCCGTGAGCCAGTT
CTATCACGAGAACCTGTG
CTGTCCCGTGAGCCAGTT|| || || || || ||
LSREPV
LSREPV||||||
CTATCACGAGAACCTGTG
TTGTCCCGGTCGCCAGTT | || | || ||
LSREPV
LSRFPV||| ||
67 100
44 80
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Always Compare Protein Sequences
ATGAATGCAGCCTATGATTGCCGAGCCAGAATGCTAAGG MNAAYDCRARMLR ||||| || || || || || || || ||||| || || | ||||||||+||ATGAAGGCCGCATACGACTGTCGTGCTAGAATCCTGAGA MKAAYDCRARILR
DNA comparison amino acid comparison
The DNA sequence can change while the amino acid sequence stays the same so always look for similarities by comparing amino acid sequences
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Exercise 1nucleotide vs amino acid search
Go to the file example-sequenceshtml and locate the section for this exercise There should be two sequences lsquosurfeit1rsquo for frog and fly
Go to NCBI Blast home page then lsquoAlign two sequencesrsquo (bottom left lsquospecialrsquo panel) paste one sequence into each window and hit lsquoAlignrsquo ndash this will do a direct DNADNA comparison
Now find the open reading frames of the two genes and translate them into amino acid protein sequences then repeat the two sequences comparison
Go to NCBI ORF Finder ndash paste sequence ndash hit OrfFind ndash identify longest ORF ndash click on it ndash next screen hit Accept ndash change View to Fasta protein ndash hit View ndash copy sequence to Blast2Seqs Do the same with the other sequence
Before you hit lsquoAlignrsquo change the lsquoProgramrsquo (top left) to blastphellip
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Answers Exercise 1
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
The Essential Taskexperiment data mining
gene sequence what is its function
database of proteins in other species
Cyclin-AFoxA1
cdc25
alpha-tubulin
Predicted protein
Gravin-like
Sprouty-2
calmodulin
KIAA10786568
frizzled
Wint8
Troponin T3
Gravin-like
we can only do this because of implied function based on orthology
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Functional Orthologs
function known annotation lsquoGravinrsquo available
Human geneXenopus genefunction unknown
sequence similarityorthologs
same function But we know that function is largely determined by shape
similar shape
Which in general we cannot determine ndash but it is probably SHAPE not SEQUENCE that is conserved
We make an assumption that the same gene function is likely to be present in the two organisms and the ones that have this function are likely to be the most similar in sequence
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Finding OrthologsSo how do we find orthologs and can we know when we have
The simplest is Reciprocal Best BLAST but it implicitly relies on having all the protein sequences of you own organism and the one you wish to find an ortholog in
frog proteindatabase of human proteins
best match human protein
database of frog proteins
x
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Using Synteny is Better
We know that large regions of (say) vertebrate genomes have preserved their overall organisation from one organism to another
And we find the same genes (ie orthologs) in more or less the same order in the syntenic sectionsThese of course represent chromosomal re-arrangements since these organisms diverged
Human chromosome 5
Mouse chromosome 10
Mouse chromosome 2
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
MetazomeFortunately someone has done all the hard work for ushellip Dan Rokhsar httpwwwmetazomenet
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Metazome Exercise
Go back to Entrez Gene and look for your favourite gene again
Pick probable ortholog vertebrate genes from common organisms (human mouse rat chicken frog fish) and paste their protein sequences into a temporary space
Go to Metazome (httpwwwmetazomenet) find the blast window open two versions of it and blast your sequences against the Tetrapod or Jawed vertebrate node
See if you get the same cluster ID as best top hit and have a look at the Metazome alignment(s)hellip
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Part 3 Finding Sequence Similarities
We want computer programs which will compare sequences at all possible different alignments looking for a degree of similarity greater than we would expect to find by chance
But first we have to consider the implication of gapshellip
Insertions and deletions are other possible forms of mutations and they can really mess up our simple alignments
ATGCATGCTGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| ||| | ||||||
ATGCATGCTGGCCAACGGATGTCCTG
ATGAAAGCCGCCTACGAAAGTCCTG||| | || ||| | | | |
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Gaps in Alignments
Consider these two obviously similar sequences
TTCCCAACTCTCCTCTTTCACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA | || | || |||||||||||||||||||| ||||||||| ||| ||| | ||| | | |TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCCAGAA
In fact we realise that the most probable alignment (regarding biological origin) is with a small gap in each sequence
TTCCCAACTCTCCTCTTT=CACCATGAAGCTCAAGGACAGATTCCACTCGCCCCAAAATCAAGCTCACCCCGTCCAAGAA |||||| ||||||||||| |||||||||||||||||||| ||||||||| |||||||||||||| |||||||||| ||||TTCCCACCTCTCCTCTTTGCACCATGAAGCTCAAGGACAAATTCCACTC=CCCCAAAATCAAGCGCACCCCGTCCCAGAA
So in general we allow ourselves to insert gaps until we find the optimal alignment
But where should this process stop
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
The Downside of GapsTake two random sequences with no lsquorealrsquo similarity
GACACTAGGTCGATGCGTGGTGGCGAGA
ACGCATCCGGATGTGCACCGTGGAACTG
And allow lsquocost freersquo gaps
GAC--ACT----AGGTCGATGC---GTGG---TGGCGAGA || | | | | | ||| |||| || ACGCA-TCCGGA--T-G-TGCACCGTGGAACTG
Clearly although the alignment has no mismatches it is obviously not biologically meaningful
To prevent this we assign a cost to adding gaps which is offset against the benefit of finding matches ndash and this is the essence of lsquofinding gapped alignmentsrsquo
We want to find the lsquoalignmentrsquo between the two (or more) sequences which shows the greatest degree of similarity while introducing the fewest gaps hellip
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
BLAST
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to find similarities between sequencesThey range from relatively slow programs which find the exact best matching alignment through ones which take progressively inexact shortcuts to speed things up Of this latter class the best known and easily most widely used is BLAST developed by Stephen Altschul and others and continuously refined over the last 10-15 years
The essential idea is to compare your query sequence against a collection or lsquodatabasersquo of target sequences looking for the one(s) that match the query sequence the best
gttarget1AAAACAGGAATATTTACCGGGACCGGGTAATGATGCATCTCGAGGTACACAATATACCTG GAGAACCGAATTATGAGTTGGCCACCTTACTTAACGAAACCAGCAGAGAAAATCCAACAT GGCAACACCCCTCTGACTACACTAGAAGGAACTACTATGTAAGAAAACAGCCTGTCCCTT GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGgttarget2CTCTTAATTTATTTCTCTTCCTGCAGCTCCCTCGCTTTTTCCTTTCCCTGTTACATTCAT CTGACTTGAAGAGTTGCAAATTTTCAGTGTTTCTGTTTTTGTTGCTGATATGTTGTAAAC TTTTTAATAAAATCTATTTCTATAG gttarget3GCAGTTTGAATGACTGGGTGATGCGAAATGGGGGTCCTGCCATAGAGCGCTTCCATGGTT TACCTTGCACATTTCAGAGAAGTCCTATGCCAGGAGTCCTTCCTACAGGGCCTTCCTGAA ACTATATATGTGCTTATTCTTGTTTGATTTGGCTTTGCAGCTAGGGTTTTCACCTTTTCT GGAAAAAAAAATACTGGCTTCC gttarget4CTGCTATTAATGGGCAAAACAACTCAAATAAAGTCCCTCTGCCACCCTCAGACACTGCCC CTGGCCCCCAGCTGCCCGCTGATCCTTGTAGCCAGAGCAGTAAAGTTTTGAAAGTGGAGC CCAAGGAGAATAAAGTTATTAAAGAAACTGGCTTTGAACAAGGTGAAAAGTCTTGTGCAG CACCTCTAGATCATACTGTGAAGGAAAATCTTGGACAAACTTCTAAAGAACAGGTGGTAG
query
database
COMPARE
LIST MATCHES
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Flavours of BLAST
ACGATAGATCCCATCCATAAAT ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
MKJLSPWERSYTRGHYTWER MGHTVNBZY MKLPWRHGDBKJGMNDFD MBKLRPIUHDFRTASGSLKWWRTVBN
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
query sequence other operation database sequences
ATGACGATAGATCCCATCAT CGATAGGACCACCACA GATAGACCAGGATACATAGGATAATTA AGCTCGCTTGGCTCGATGGCT
BLASTn
BLASTp
BLASTx
tBLASTn
tBLASTx
ACGATAGATCCCATCCATAAAT
ACGATAGATCCCATCCATAAAT
MQWCGYRWTYQGYRW
MQWCGYRWTYQGYRW
FAST
FAST
SLOW
SLOWER
HORRIBLY
SLOW
6 fra
me
trans
latio
n
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
How does it work The main task of any sequence comparison program is to test all possible mutual alignments of two sequence and see how good the match is
CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT
CCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTC | | | | | ||||||||||||||||||||||||| CTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGTCTTCTCATTGCTCTTCCTAACAGTGATGATAGGCTAACCGTAATGGCGTTCAGGAGT || | | | | | | | | |||||||||||||||||||||||| | | | | | |
CCGAGCTTCTCATTGCTCTTCCTAACAGTG=TGATAGGCTAACCGTAATGGCGTTC||||||||||||||||||||||||| ||||||||||||||||||||||||
query
1st database sequence
This would actually be a very slow search process if implemented like thishellip
BLAST achieves its speed through two strategies
- it takes a WORD based approach- it pre-INDEXES database sequences
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
BLAST WORDS and INDEXING1 GACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
2 TAAGCAAATTTAATTTTGTTTACATTTTC
3 GTTAAGACCTTCCCTGACATTTGCAGCAGTTTCAAATGTA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Database of sequences
Numbered list of all possible lsquowordsrsquo
Build a position index of all words in the database
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Analyse the Query Sequence gtquery AGACAAATCCAAACCCCTGAAGTTCTCCACCAGCAAAGCCA
AAAAAAAA 00001 AAAAAAAC 00002 AAAAAAAG 00003 ACAAATCC 07967 ACAAATCC 07968 ACAAATCC 07979 GACAAATC 33568 GACAAATG 33569 TCCAAACC 64321 TCCAAACC 64322
QUERY SEQUENCE
Numbered list of all possible lsquowordsrsquo
position word
1 14236
2 33658
3 07967
Analyse QUERY SEQUENCE
sequence position word
1 1 33658
1 2 07967
1 3 16210
3 15 33568
3 16 07967
Index of database
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Expand from Word Based Matches We lsquoinstantlyrsquo know which sequences in the database have at least a word length match with our query sequence and at what relative position
Next the potential alignments are expanded adding up a score for (total matches ndash mismatches ndash gap penalties) to make the best possible alignment But this is usually for a tiny proportion of the sequences in the database ndash so overall it is much quicker
The highest scoring alignments are reported
But we can potentially miss alignments with no word-size bits in common consider BLASTn with a default word-size of 11
TCGGAAGTGGAAGCTGAACCTGATTGTAGAGTTGGAGGCCAGTGTTCTGGCTGAGC||||||||| ||||| |||||||||| |||||||||| |||| ||||| ||||||| TCGGAAGTGTAAGCTCAACCTGATTGCAGAGTTGGAGTCCAGAGTTCTAGCTGAGC
Care is sometimes neededhellip
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
BLAST ndashTypical OutputINPUT
gtpartial cDNA sequence Xenopus tropicalisCGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGTTCCCACCTCTCCTCTTTCACCATGAAGCTCAAGGACAAATTCCACTCCCCCAAAATCAAGCGCACCCCGTCCAAGAAGGGGAAGCCGGCCGACCTCACCGTCAAAACAGAAGAGAAACCCGTCAACAAAACCTTAAGCCGCTTGGAGGAACAGGAGAAAGAAGTCGTTAATGCCTTGCGTTACTTTAAGACAATTGTTGACAAGATGGCGGTGGACAAGATGGTGCTGGTGATGCTGCCAGGGTCGGCGA
OUTPUTQuery= (311 letters) Database NCBI Protein Reference Sequences 954378 sequences 347895532 total letters
gtgi|41055060|ref|NP_9574201| similar to guanine nucleotide-releasing factor 2 (specific for crk proto-oncogene) [Danio rerio]
Length=691
Score = 133 bits (335)Expect = 6e-31 Identities = 7698 (77) Positives = 8298 (83) Gaps = 498 (4) Frame = +2
Query 26 MSGKIE-KADSQRSHLSSFTMKLKDKFHSPKIKRTPSKKGKPA--DLTVKTEEKPVNKTL 196 MSGKIE K +SQ+SHLSSFTMKL KFHSPKIKRTPSKKGK + VKT EKPVNK + Sbjct 1 MSGKIESKHESQKSHLSSFTMKLM-KFHSPKIKRTPSKKGKQLQPEPAVKTPEKPVNKKV 59
Query 197 SRLEEQEKEVVNALRYFKTIVDKMAVDKMVLVMLPGSA 310 SRLEEQEK+VV+ALRYFKTIVDKM VD VL MLPGSA Sbjct 60 SRLEEQEKDVVSALRYFKTIVDKMNVDTKVLQMLPGSA 97
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
When is a match significant
RFKISDCQHPCTYSHNQYMTNHMRECPYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV
NFSWKKTSEKETNCQFDYPNDYNEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFNMCWLEVNSS
RF---KISDCQHPCTYSH-NQYMTNHMREC----PYNGAATSIPSWHLIVHPSNGQSVSFPQSDPCQIKMNQNLHLVQMMYDMQTTHV F K S+ + C + + N Y N +C P+ + +W +P + D I N M ++ NFSWKKTSEKETNCQFDYPNDY--NEQTQCQPMTPFKADVFDLWNWEFNANPKLENGIRDLIDDKHDILQIFN------MCWLEVNSS
Here is a lsquotypicalrsquo weak alignment from BLASTp
In fact the sequences were randomly generated so there is no biologically significant alignmenthellip
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
E-values
The number of matches like the discovered match that I would expect to find by chance
An E-value of 00 implies that I would expect no matches like this to arise by chance thereforehellip
An E-value of 1 implies I would expect 1 match like this to arise by chance so if I have a match with such an E-valuehellip
Also ldquoexpect valueldquo or ldquoexpectationrdquo
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
E-values From First Principles
Some database statistics (23rd July 2005)
Database NCBI RefSeq mRNA 272619 sequences 503566580 total letters (~50 x 108)
Database NCBI nr 3329110 sequences 14601814750 total letters (~14 x 1010)
Notation
12e-35 = 12 x 10-35
48 x 106 = 4800000
We will consider first searching a nucleotide sequence (lsquoACGTAGACGTrsquo) against a nucleotide database eg the RefSeq mRNA above
Then we will consider the more complex case of amino acid sequence (protein) searches Which is of course what we mostly do
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Calculating an E-valueThe RefSeq mRNA database has ~ 50 x 108 letters There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (50 x 108) 4 = ~12 x 108
Expected number of matches = (50 x 108) (4x 4) = ~31 x 107
Expected number of matches = (50 x 108) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (50 x 108) (4 x 4 x 4 x 4 hellip 60 times ) = (50 x 108) 1036 = 50 x 10-28
E-value = 50 x 10-28
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
E-values In PracticeSo if I take a 60 nt sequencegtsequenceACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA and actually BLAST it against the RefSeq mRNA database I get
BLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 2e-26 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
What do I get if I BLAST it against the larger nr databaseBLAST OUTPUTgtgi|27469838|gb|BC0417101| Homo sapiens Rap guanine nucleotide exchange factor (GEF) 1 transcript variant 2 mRNA (cDNA clone MGC49019 IMAGE6051007) complete cds
Length=6060 Score = 119 bits (60) Expect = 6e-25 Identities = 6060 (100) Gaps = 060 (0) Strand=PlusPlus
Query 1 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 2977 ACAGCTCGTCCTCCTTCCGAGCCTACCGGGCCGCCCTCTCGGAGGTGGAACCGCCGTGCA 3036
theoretical value was 50e-28 -
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
E-value Exercise
Given a transcription factor binding site
ACC[TG]TA
How many would you expect to find by chance in a 10k promoter sequence
How would this differ if there was an optional additional base between the 4th and 5th positionsIeACC[TG]TAOR ACC[TG]TA
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
E-value Exercise AnswerACC[TG]TA
Expect lsquoArsquo every 4 ntExpect lsquoACCrsquo every 4x4x4 = 64 nt
Expect lsquoT or Grsquo every 2nd ntExpect lsquoACC[TG]rsquo every 64x2 nt = 128 nt
Expect lsquoTArsquo every 4x4 = 16 ntExpect lsquoACC[TG]TArsquo every 128x16 nt = 2048 nt (4x4x4x2x4x4)We would expect ~5 of these promoter sites every 10k by chance
If also ACC[TG]TAA allowed
The two motifs independently have the same E-valueTo allow either means we expect twice as many
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 letters (was RefSeq and 50 x108)There are 4 possible nucleotides - ACGT How many matches do we expect to find by chance
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGA A A A A AA A A A AA AA
Query = lsquoArsquo
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACrsquo
AC AC AC AC
CCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGQuery = lsquoACGrsquo
ACG
Expected number of matches = (14 x 1010) 4 = ~12 x 108
Expected number of matches = (14 x 1010) (4x 4) = ~31 x 107
Expected number of matches = (14 x 1010) (4 x 4 x 4) = ~81 x 106
Query = lsquoACGTCGAhellipCTGATTCGrsquo - 60-mer
Expected number of matches = (14 x 1010) (4 x 4 x 4 x 4 hellip 60 times ) = (14 x 1010) 1036 = 14 x 10-26
E-value = 14 x 10-26
(was E-value = 50 x 10-28)
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
E-values Effect of Database Size
The E-value is simply dependent on database size
RefSeq
nr
14 x 1010 letters
50 x108 letters
30 x bigger
BLAST the same sequenceagainst each E-value = 14e-26
E-value = 50e-28
The database was ~30 times bigger and so the E-value was ~30 times bigger
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Why were the values differentOur calculated E-value for searching against the RefSeq mRNA database was 50 x 10-28But our actual BLAST search at NCBI gave a value of
20 x 10-26 - about 40x larger - why is this
Gapped alignments
If we were expecting N matches for a query sequence lsquoACGTACGTACGTrsquo imagine what would happen to N if we allowed gaps in our matches
ACGTACGTACGT
This would now give us additional possible alignments that would meet our lsquomatchrsquo criteria
ACGTACGTACGT ACGTACAGTACGT ACGTACCGTACGT etc|||||||||||| |||||| |||||| |||||| ||||||ACGTACGTACGT ACGTAC-GTACGT ACGTAC-GTACGT
We will expect many more matches in a given database if we allow our alignments to have gaps The E-value will be larger
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
E-values Effect of Query Length
Biologically itrsquos the same match Does it mean we are any less sure that this match didnrsquot occur by chance The E-value is simply dependent on match length
database
BLAST 500 nt sequence against a database
BLASTn Get a full length match with sequence XYZ at an E-value = 50e-160
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCGATCGATCGCGCATCGATCGTCTAGATCGATCGCTCGCTGTGTAGATAGATCGGCGATAGA
database
BLAST half of the same sequence against the same database
BLASTn
gtsequence ACTAGTCTAGCTAGACATCGATCGATGATGCTACACAGATAGACGATAGATAGTAAGTCG
Get a match with sequence XYZ again but at an E-value = 50e-80
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Why not just use identityAt some levels this a good question
But consider two very different searches both of which give a 75 identity match
Query1 was 60 nt longCGGAGCTCAGGGCTTAACGACTGATATCTCCGCGCATGTCGAGAAACGATACAGCCAGCG||||||||||| || | || | || || |||| | | | |||||| | ||||||||||CGGAGCTCAGGCCTCACCGGCGGACATGTCCGGGAAAATAGAGAAAGCAGACAGCCAGCGWhich would have an E-value ~ 50 x 10-19
And Query2 only 16 nt longACGTACGTACGTACGT||| || | |||| ||ACGCACCTTCGTAGGTWhich would have an E-value ~ 30
And intuitively we feel we would expect to see that sort of number of matches in the database just by chancehellip
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
So whatrsquos the real problemBasically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
Are there any useful guidelines though at least for biological meaningfulness
Basically you are usually trying to answer the question
Can I find the ortholog of my gene in some other species so that I can work out what it might be doing in my organism
BLAST
The difficulty is because
ORTHOLOGY
BLAST Similarity + Probability
biological knowledge
nature of query sequence
phylogenetic relationship
match length PI size of databasehellip
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Rules of ThumbHow good does an E-value have to be before we might even think we have an ortholog
largerworse smallerbetter
E-values 10-5 10-10 10-40 10-100 00
fantasy borderline encouraging
pretty good canrsquot get
better
But note that in some gene families with closely related members you can get an E-value of 00 for several different matches and then identity may be more sensitive Also bear in mind in cases like this that ideas of lsquofunctionalrsquo orthology may break down with more than one locus producing identical proteins which share the same functionhellip
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because the amino acid sequence is more conserved than the underlying DNA sequenceDoes this cause us to treat expected values any differently
If we follow the argument as before then for an exact match of a 20 amino acid sequence in the RefSeq protein database each additional amino acid will reduce the E-value by 120th (there are 20 different amino acids) And as there are 347895532 letters in that databaseE-value = ~35 x 108 (20 x 20 x 20 hellip20 times) = ~35 x 10-18
But this is what we get if we run the blast at NCBI
Score = 431 bits (100) Expect = 8e-04 Identities = 2020 (100) Positives = 2020 (100) Gaps = 020 (0) Frame = +3
Query 3 SSSSFRAYRAALSEVEPPCI 62 SSSSFRAYRAALSEVEPPCISbjct 972 SSSSFRAYRAALSEVEPPCI 991
Really too big a discrepancy to easily explain with hand wavinghellip
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Amino Acid Substitutions
A SC F LWYG I LMVL IMFVM ILVP V ILMW FY
N DHSQ REHKS ANTT SY HFW
H NQYK RQER QK
D NEE DQK
In fact we need to take into account both amino acid substitutability as well as as before allowing gapped alignments On average any residue can be substituted for by about 2 others so each position has about 17th chance of lsquomatchingrsquo rather than 120th
So now we getE-value = ~35 x 108 (7 x 7 x 7 hellip20 times) = ~44 x 10-9which is much closer to the actual BLAST value
These substitutabilities are dealt with by the BLOSUM and PAM matrices
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
ExercisesGo to the file random-DNA-sequenceshtml select one of the 20 randomly generated nucleotide sequences and do a BLASTx (translated DNA-gtprotein) at NCBI against the nr protein database
Did you find any lsquosignificantrsquo hits
Repeat with a second sequence
What conclusions might you draw from this exercise
Try the same sequence(s) against the nr nucleotide database
Is there any general difference
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Part 4 Tweaking BLASTAlthough you normally see BLAST as a web page with boxes to place data in and tick boxes etc it is actually a command line program that can be run just by typing the appropriate command and options eg
promptgt blastall ndashp ltblast typegt ndashi ltinput sequencegt ndashd ltdatabasegt
This is the simplest form where the basic program lsquoblastallrsquo takes a number of different options or parameters indicated by the ndashx and followed by its value -p ltwhich blast flavour to rungt-i ltfile with query sequence ingt-d ltpre-indexed database namegt
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Not All Parameters are hellipThere are many other parameters and if not listed explicitly they will use a default value most appropriate to the blast flavour requested Eg for ndashW ltword sizegt blastn uses ndashW 11 where blastx uses ndashW 3
There are also some options that appear on the web pages that are not really parameters but manage the job in a similar way One of the most useful of these is on the NCBI blast pages where you can use Entrez queries or pick from an organism list to modify your search
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
The Many Parameters of BLASTThere are almost literally hundreds of parameters but most are way too obscure even for die-hard techies like me Very few of them are regularly useful in any but their default value but just occasionally they are very necessary
Here are some of the ones that I have used
-e max expected value -m output format (graphical or tabularspreadsheet)-F filter query sequence for low complexity (default TRUE)-U use only upper case regions of query (default FALSE)-G gap opening cost-E gap extension cost-q nucleotide mismatch penalty (BLASTx uses matrices)-r nucleotide match reward-b number of matching sequences to report-g allow gaps (default TRUE)-W word size-z effective database size (removes effect of actual database size)-S query strands to search (default both directions)-l restrict database sequences to given list of lsquogilsquo numbers
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
BLAST Parameters Exercises1 BLASTn vs BLASTx
Open the file example-sequenceshtml copy the sequence gtblastn-vs-blastxThis is a Xenopus tropicalis cDNA sequence
Go to the NCBI BLAST Home PageNucleotide-nucleotide BLAST (blastn) section Paste your sequence into the box
Run BLASTn against the nr nucleotide database using all default optionsThen hit [format] to wait for the results in a new page(hint if you paste the sequence definition line lsquogtnamersquo into the box as well your results will be labelled accordingly which can be useful)
Now repeat but go to the TRANSLATED BLAST section and BLAST against the nr protein database using BLASTx
How might the different results help us view the presence of this gene in other vertebrates
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Results for Exercise 1 BLASTn
BLASTx
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
BLAST Parameters Exercises2 Low complexity filtering
Open the file example-sequenceshtml copy the sequence gtlow-complexity-filtering-A This sequence contains a long AT tandem repeat
Go to the NCBI BLAST Home PageTRANSLATED BLAST sectionBLASTx Paste your sequence into the box
Carefully UNTICK the ldquoChoose filter [ ] Low complexityrdquo BOX in the second section And then run BLASTx against the nr database
What do you feel about these alignmentsRe-run but leave the low-complexity filter ON this timeDoes this change our view of the protein matches
Now continue with gtlow-complexity-filtering-B and ndashCC is an especially interesting case ndash what can we deduce about the cDNA sequence Annotators beware
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Results for Exercise 2A (OFF) BLASTn ndash low complexity filtering OFF
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Results for Exercise 2A (ON) BLASTn ndash low complexity filtering ON
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Results for Exercise 2B
ON OFF
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Results for Exercise 2C
There is a sequence error an extra G at position 117 in the sequence cDNA (117)AGAAAAGAAGAAACATGGCAATGGATCAGAA|||||||||||||||| ||||||||||||||AGAAAAGAAGAAACAT-GCAATGGATCAGAA
Genomic sequence
ON
OFF
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
BLAST Parameters Exercises3 Limit by Entrez query
Entrez queries can be used in the NCBI BLAST web page to restrict the search to more specific items For instance to find only matching sequences in fruit fly enter lsquoDrosophila melanogaster[ORGN]rsquo in the Limit by entrez query box in the second section (you can also select the organism from the adjacent drop-down list) To combine items use logical AND OR or NOT
Open the file example-sequenceshtmlCopy the sequence gtcyclin-D1-Xt and go to the NCBI BLAST Home Page TRANSLATED BLAST sectionBLASTx and paste the sequence
Use an Entrez query to find all rodent sequences (rat and mouse) with a good match to cyclin-D1 At what E-value do we expect we are no longer looking at cyclins Try running the search again with that E-value as a limithellip
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
BLAST Parameters Exercises4 BLASTn vs tBLASTx and nucleotide mismatch penalties
Open the file example-sequenceshtml
Also open the NCBI BLAST Home PageSPECIAL ndash Align two sequences section
There are several Xenopus tropicalis cyclins in the examples fileCopy the sequence gtcyclin-A1-Xt to the Sequence 1 BLAST windowCopy the sequence gtcyclin-A2-Xt to the Sequence 2 BLAST window(i) Run the default comparison should be BLASTn Note the alignmentNow run again using tBLASTx ndash what does this do to our understanding of the relationship between these two sequences Are they homologs orthologs or paralogs ndash or none of these
(ii) Revert to BLASTn and try varying the values for mismatch penalties and gapping ndash start by reducing the mismatch penalty to -1 Then try reducing the gap open and gap extension penaltieshellipWhat do we learn from this
(iii) Now repeat the first parts of the exercise with cyclin-D1 in place of cyclin-A2hellip
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Results for Exercise 4 (i)
BLASTn tBLASTx
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
Results for Exercise 4 (ii)
Mismatch penalty = -2 (default) Mismatch penalty = -1
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
BLAST Parameters Exercises5 E-Value maximum for reporting
Open the file example-sequenceshtml
Copy the sequence gtsumo-binding-motif and go to the NCBI BLAST Home PageGo to the PROTEIN BLAST section BLASTp and paste the sequence
Run the search with the default values
Now re-run the search setting the maximum E-value in the box
Expect 100
What difference does this make
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
BLAST Parameters Exercises6 Word Size
Open the file example-sequenceshtml
Copy the sequence gtmorpholino and go to the NCBI BLAST Home PageGo to the NUCLEOTIDE BLAST section BLASTn and paste the sequence
Check OFF the low complexity filter and then run the search
Now re-run the search setting the following parameters
Low complexity OFFExpect 100Word Size 7Other advanced -q-1 (mismatch penalty -1 instead of default -3)
What difference does this make
END
END
