Embed Size (px)
Citation preview
Finding Sequence Similarities
gtqueryAGACGAACCTAGCACAAGCGCGTCTGGAAAGACCCGCCAGCTACGGTCACCGAGCTTCTCATTGCTCTTCCTAACAGTGTGATAGGCTAACCGTAATGGCGTTCAGGAGTATTTGGACTGCAATATTGGCCCTCGTTCAAGGGCGCCTACCATCACCCGACGGTCATGCCGGTCCCCAGCAGCTGCTAATAACTTCCTTCGCTACTCAAGTTACCACGCTAGCAAAACCCACGGCATACCGTTTACCCTTTAAAATCAGCTTCAACCAGCAACGAA
There are many programs used to do thisThey 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 BLAST can perform a number of similar tasks with different types of sequence
BLASTn ndash comparing nucleotide sequence vs nucleotide sequence database - FAST
BLASTp ndash comparing protein sequence vs protein sequence database - FAST
BLASTx ndash comparing nucleotide sequence vs protein sequence database by translating the nucleotide sequence in all possible reading frames - SLOW
tBLASTn ndash comparing protein sequence vs nucleotide sequence database translated into all possible reading frames - SLOWER
tBLASTx ndash comparing nucleotide sequence vs nucleotide sequence database translating both into all possible reading frames ndash EXCRUCIATINGLY SLOW
The amino acid sequence based programs use a substitution matrix to allow some amino acids to count as effective matches with each other These are the BLOSUM and PAM matrices you may see referred to from time to time
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
BLAST achieves its speed through two strategies
It lsquoindexesrsquo the database sequences so it know where all the minor subsequences are in each sequence so it doesnrsquot have to look all the way through each sequence each time letter by letter
Itrsquos lsquoword basedrsquo so that it will only start looking for possible extensive alignments once itrsquos found a seed alignment of an exact match The default seed lengths are 11 letters for BLASTn and 3 for BLASTp This means that some good alignments are un-findable eg a 50 protein match with exactly every second amino acid matching It relies on these lsquouniformly distributedrsquo alignments being very rare occurrences
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
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-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
Flavours of BLAST BLAST can perform a number of similar tasks with different types of sequence
BLASTn ndash comparing nucleotide sequence vs nucleotide sequence database - FAST
BLASTp ndash comparing protein sequence vs protein sequence database - FAST
BLASTx ndash comparing nucleotide sequence vs protein sequence database by translating the nucleotide sequence in all possible reading frames - SLOW
tBLASTn ndash comparing protein sequence vs nucleotide sequence database translated into all possible reading frames - SLOWER
tBLASTx ndash comparing nucleotide sequence vs nucleotide sequence database translating both into all possible reading frames ndash EXCRUCIATINGLY SLOW
The amino acid sequence based programs use a substitution matrix to allow some amino acids to count as effective matches with each other These are the BLOSUM and PAM matrices you may see referred to from time to time
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
BLAST achieves its speed through two strategies
It lsquoindexesrsquo the database sequences so it know where all the minor subsequences are in each sequence so it doesnrsquot have to look all the way through each sequence each time letter by letter
Itrsquos lsquoword basedrsquo so that it will only start looking for possible extensive alignments once itrsquos found a seed alignment of an exact match The default seed lengths are 11 letters for BLASTn and 3 for BLASTp This means that some good alignments are un-findable eg a 50 protein match with exactly every second amino acid matching It relies on these lsquouniformly distributedrsquo alignments being very rare occurrences
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
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-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
BLAST achieves its speed through two strategies
It lsquoindexesrsquo the database sequences so it know where all the minor subsequences are in each sequence so it doesnrsquot have to look all the way through each sequence each time letter by letter
Itrsquos lsquoword basedrsquo so that it will only start looking for possible extensive alignments once itrsquos found a seed alignment of an exact match The default seed lengths are 11 letters for BLASTn and 3 for BLASTp This means that some good alignments are un-findable eg a 50 protein match with exactly every second amino acid matching It relies on these lsquouniformly distributedrsquo alignments being very rare occurrences
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
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-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
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-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
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-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
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-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
E-values Effect of Database SizeThe nr mRNA database has ~ 14 x 1010 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 = (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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
And the difficulty is because BLAST does not set out to address questions like orthology BLAST only tells you about sequence similarity with some notion of how likely a similarity is to have arisen by chance based on some general biological principles
You will always have to add in your own knowledge of biology and exactly what your query sequence was and how it is related to your matching sequences In particular whether the degree of similarity matches up to the supposed evolutionary distance between the two species You will also need to take into account the length of the reported match compared to the lengths of your query and matched sequences And of course the size of the database
Are there any useful guidelines though
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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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 there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
Protein BLASTItrsquos (nearly) always better to make comparisons at the amino acid level between protein sequences than the DNA level because there are many different DNA sequences that can give exactly the same protein 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 of 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
ExercisesGo to the file random-DNA-sequenceshtml randomly 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
