1

Sequence alignment methods

ATGAAAAAGAAAACAACACTTAGCGAGGAGGACCAGGCTCTGTTTCGCCAGTTGATGGCG

GGGACTCGCAAGATTAAGCAGGACACGATTGTCCACCGACCGCAGCGTAAAAAAATCAGC

GAAGTGCCGGTGAAACGCTTGATCCAGGAGCAGGCTGATGCCAGCCATTATTTCTCCGAT

GAGTTTCAGCCGTTATTAAATACCGAAGGTCCGGTGAAATATGTTCGCCCGGATGTCAGC

CATTTTGAGGCGAAGAAACTGCGCCGTGGCGATTATTCGCCGGAGTTGTTTTTGGATTTA

CACGGTCTGACGCAGCTGCAGGCCAAGCAGGAACTGGGGGCGTTGATTGCCGCCTGCCGCPstI

GAGTTGCCCTGATAAGGGTACTATTACGGACGAGTCATCTTATGCGGAGCGATTAGGGCG

CGGTTAGCGAGCTACTATCGGGGGGCGAGCTTATTGGGCGGGGCGGACTATGGGCTGGCG

AGGCGGAACGGGTACTGGACGTACTAGGCGAGGCGATCTAGCGAGGGCATGTTGATGGCG

GGAGCGGTTTTTAGGGCGTTTTTGGCGGCCCCCTATCTATGCAGCACGAGCGACTATGCC

Word/pattern recognition-Identification of restriction enzyme cleavage sites

The universe of biological sequence analysis

CGCCGAGGATGGCCGTCATGGCGCCCCGAACCCTCCTCCTGCTACTCTTGGGGGCCCTGG

MetAlaProArgThrLeuLeuLeuLeuLeuLeuGlyAlaLeuAla

CCCTGACCCAGACCTGGGCGGGTGAGTGCGGGGTCGTGGGGAAACCGCCTCTGCGGGGAG

LeuThrGlnThrTrpAlaGly

AAGCAAGGGGCCCGCCCGGCGGGGACGCAGGACCCGGGTAGCCGCGCCGGGAGGAGGGTC

GGGTGGGTCTCAGCCACTCCTCGCCCCCAGGCTCCCACTCCATGAGGTATTTCACCACAT

SerHisSerMetArgTyrPheThrThrSer

CCGTGTCCCGGCCCGGCCGCGGGGAGCCCCGCTTCATCGCCGTGGGCTACGTGGACGACA

ValSerArgProGlyArgGlyGluProArgPheIleAlaValGlyTyrValAspAspThr

CGCAGTTCGTGCGGTTTGACAGCGACGCCGCGAGCCAGAGGATGGAGCCGCGGGCACCGT

GlnPheValArgPheAspSerAspAlaAlaSerGlnArgMetGluProArgAlaProTrp

GGATAGAGCAGGAGGGGCCGGAGTATTGGGACCTGCAGACACGGAATGTGAAGGCCCAGT

IleGluGlnGluGlyProGluTyrTrpAspLeuGlnThrArgAsnValLysAlaGlnSer

CACAGACTGACCGAGCGAACCTGGGGACCCTGCGCGGCTACTACAACCAGAGCGAGGCCG

GlnThrAspArgAlaAsnLeuGlyThrLeuArgGlyTyrTyrAsnGlnSerGluAla

GTGAGTGACCCCGGCCCGGGGCGCAGGTCACGACCTCTCATCCCCCACGGACGGGCCGGG

Exon 1

Exon 2

- prediction of exon structureThe universe of biological sequence analysis

C G A T A G C A T G A T G T C TC G A C A G C A T - A T G T C T* * * * * * * * * * * * * *

Pairwise alignment

2

Why sequence alignments ?

• Prediction of function • Protein family analysis• Comparative genomics• Phylogeny / Evolutionary history• Genome sequencing:

• Assembly• Alignment to reference genome

We have a ‘new’ sequence. It is similar to a previously known sequence?We can test by alignment whether it is similar

to a sequence with known function. If it is we can assign a possible function to our new sequence

Prediction of function

Sequence to be investigated

Database of sequences

Seq. with known function

Protein family analysis Comparative genomics - reveals biologically significant regions of the genome

3

dotplotC G A

CGACAGCATATGTCT

T A G C A T G A T G T C T

CGATAGCATGATGTCTCGACAGCAT-ATGTCT*** ***** ******

Pairwise alignmentdotplotPairwise alignment C G A

CGACAGCATATGTCT

T A G C A T G A T G T C T

dotplotPairwise alignment C G A

CGACAGCATATGTCT

T A G C A T G A T G T C T

CGATAGCATGATGTCTCGACAGCAT-ATGTCT*** ***** ******

- -2221222222222222 = 25+ + + + + + + + + + + + + +

dotplotC G A

CGACAGCATATGTCT

T A G C A T G A T G T C T

CG-----ATAGCATGATGTCTCGACAGCATA------TGTCT** *** *****

- -222222222222222222222 = -2- - - - - -- - -+ + + + +++++ +

Pairwise alignment

4

More sophisticated scoring of protein sequence alignments

Each amino acid change has acharacteristic probability

substitution matrix

More sophisticated scoring of protein sequence alignments

Each amino acid change has acharacteristic probability

A G L C E| | | | |A A L C D4+ 0+4 +9+2 =19

A

B

Local alignment

Global alignment

AB

| | | | | | | | | |

| | | | | | | | | | | | | |

Local and global alignments

BLAST - searches in databases for sequence similarityClustalW - multiple alignment of sequences

Frequently used methods in sequence analysis that are based on sequence alignment

5

FASTA, 1988William Pearson

BLAST

David LipmanStephen Altschul

BLAST, 1990

Searching databases for sequence similarity- traditional alignment method too slow BLAST - Basic Local Alignment Search

Tool

A query sequence (DNA or protein) is tested against all sequences in a database (DNA or protein) , i.e the query is aligned to all the database sequences. Final output is a list of the best matching database sequences.

M A K I Q G L G K R Y

M *A *K * *L *Q *G * *A *L *G * *K * *R *Y *

Improvement of speed as compared to local alignment algorithm:

* Initial search isfor word hits.

* Word hits are then extended in either direction.

Searching databases for sequence similarity- shortcuts of BLAST

"word hit"

BLASTP 2.2.9 [May-01-2004]

Reference: Altschul, Stephen F., Thomas L. Madden, Alejandro A. Schaffer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs", Nucleic Acids Res. 25:3389-3402.

Query= lcl|SRP54_MOUSE (P14576) Signal recognition particle 54 kDa protein (SRP54)(504 letters)

Database: swissprot197,228 sequences; 71,501,181 total letters

Searching..................................................done

Score E

Sequences producing significant alignments: (bits) Value

SRP54_MOUSE (P14576) Signal recognition particle 54 kDa protein ... 959 0.0 SRP54_PONPY (Q5R4R6) Signal recognition particle 54 kDa protein ... 958 0.0 SRP54_MACFA (Q4R965) Signal recognition particle 54 kDa protein ... 958 0.0 SRP54_HUMAN (P61011) Signal recognition particle 54 kDa protein ... 958 0.0 SRP54_CANFA (P61010) Signal recognition particle 54 kDa protein ... 958 0.0 SRP54_RAT (Q6AYB5) Signal recognition particle 54 kDa protein (S... 957 0.0 SRP54_GEOCY (Q8MZJ6) Signal recognition particle 54 kDa protein ... 794 0.0 SR542_LYCES (P49972) Signal recognition particle 54 kDa protein ... 565 e-161SR543_ARATH (P49967) Signal recognition particle 54 kDa protein ... 560 e-159SR542_HORVU (P49969) Signal recognition particle 54 kDa protein ... 558 e-158......SRPR_MOUSE (Q9DBG7) Signal recognition particle receptor alpha s... 99 3e-20SRPR_HUMAN (P08240) Signal recognition particle receptor alpha s... 99 3e-20SRPR_YEAST (P32916) Signal recognition particle receptor alpha s... 98 7e-20

BLAST output

6

BLAST output, cont.

sp|Q9I3P8.1|FLHF_PSEAE RecName: Full=Flagellar biosynthesis prot... 57 3e-07sp|Q44758.1|FLHF_BORBU RecName: Full=Flagellar biosynthesis prot... 55 2e-06sp|Q01960.1|FLHF_BACSU RecName: Full=Flagellar biosynthesis prot... 53 4e-06

sp|O28980.1|Y1289_ARCFU RecName: Full=Uncharacterized protein AF... 39 0.064sp|B9LKC1.1|CYSC_CHLSY RecName: Full=Adenylyl-sulfate kinase; Al... 38 0.21 sp|Q12U80.1|RADB_METBU RecName: Full=DNA repair and recombinatio... 37 0.29 sp|A5D014.1|ACCD_PELTS RecName: Full=Acetyl-coenzyme A carboxyla... 35 0.93 sp|Q03T56.1|RSMA_LACBA RecName: Full=Ribosomal RNA small subunit... 35 1.2 sp|Q1I2K4.1|CYSC_PSEE4 RecName: Full=Adenylyl-sulfate kinase; Al... 35 1.6 sp|Q38V22.1|RSMA_LACSS RecName: Full=Ribosomal RNA small subunit... 34 1.8 sp|A1U3X8.1|CYSC_MARAV RecName: Full=Adenylyl-sulfate kinase; Al... 34 2.3 sp|A6TD42.1|CYSC_KLEP7 RecName: Full=Adenylyl-sulfate kinase; Al... 34 2.9 sp|P63890.2|CYSC_SALTI RecName: Full=Adenylyl-sulfate kinase; Al... 34 2.9

...

Parameter that describes the number of hits one can "expect" to see just by chance when searching a database of a particular size. Essentially, the E value describes the random background noise that exists for matches between sequences. For example, an E value of 1 assigned to a hit can be interpreted as meaning that in a database of the current size one might expect to see 1 match with a similar score simply by chance. This means that the lower the E-value, or the closer it is to "0" the more "significant" the match is.

Expect value (E)

Query: 1 MVLADLGRKITSALRSLSNATIINEEVLNAMLKEVCTALLEADVNIKLVKQLRENVKSAI 60MVLADLGRKITSALRSLSNATIINEEVLNAMLKEVCTALLEADVNIKLVKQLRENVKSAI

Sbjct: 1 MVLADLGRKITSALRSLSNATIINEEVLNAMLKEVCTALLEADVNIKLVKQLRENVKSAI 60

Query: 61 DLEEMASGLNKRKMIQHAVFKELVKLVDPGVKAWTPTKGKQNVIMFVGLQGSGKTTTCSK 120DLEEMASGLNKRKMIQHAVFKELVKLVDPGVKAWTPTKGKQNVIMFVGLQGSGKTTTCSK

Sbjct: 61 DLEEMASGLNKRKMIQHAVFKELVKLVDPGVKAWTPTKGKQNVIMFVGLQGSGKTTTCSK 120

Query: 121 LAYYYQRKGWKTCLICADTFRAGAFDQLKQNATKARIPFYGSYTEMDPVIIASEGVEKFK 180LAYYYQRKGWKTCLICADTFRAGAFDQLKQNATKARIPFYGSYTEMDPVIIASEGVEKFK

Sbjct: 121 LAYYYQRKGWKTCLICADTFRAGAFDQLKQNATKARIPFYGSYTEMDPVIIASEGVEKFK 180

Query: 181 NENFEIIIVDTSGRHKQEDSLFEEMLQVSNAIQPDNIVYVMDASIGQACEAQAKAFKDKV 240NENFEIIIVDTSGRHKQEDSLFEEMLQV+NAIQPDNIVYVMDASIGQACEAQAKAFKDKV

Sbjct: 181 NENFEIIIVDTSGRHKQEDSLFEEMLQVANAIQPDNIVYVMDASIGQACEAQAKAFKDKV 240

Query: 241 DVASVIVTKLDGHAKGGGALSAVAATKSPIIFIGTGEHIDDFEPFKTQPFISKLLGMGDI 300DVASVIVTKLDGHAKGGGALSAVAATKSPIIFIGTGEHIDDFEPFKTQPFISKLLGMGDI

Sbjct: 241 DVASVIVTKLDGHAKGGGALSAVAATKSPIIFIGTGEHIDDFEPFKTQPFISKLLGMGDI 300

High Scoring Pair (HSP)

>SRPR_MOUSE (Q9DBG7) Signal recognition particle receptor alpha subunit(SR-alpha) (Docking protein alpha) (DP-alpha)

Length = 636

Score = 99.0 bits (245), Expect = 3e-20Identities = 68/313 (21%), Positives = 143/313 (45%), Gaps = 31/313 (9%)

Query: 14 LRSLSNATIINEEVLNAMLKEVCTALLEADVNIKLVKQLRENVKSAIDLEEMASGLNKRK 73L+ L + ++ E + ++L ++ L+ +V + QL E+V + ++ + M +

Sbjct: 322 LKGLVGSKSLSREDMESVLDKMRDHLIAKNVAADIAVQLCESVANKLEGKVMGTFSTVTS 381

Query: 74 MIQHAVFKELVKLVDPGVKAW-------TPTKGKQNVIMFVGLQGSGKTTTCSKLAYYYQ 126++ A+ + LV+++ P + + + V+ F G+ G GK+T +K++++

Sbjct: 382 TVKQALQESLVQILQPQRRVDMLRDIMDAQRRQRPYVVTFCGVNGVGKSTNLAKISFWLL 441

Query: 127 RKGWKTCLICADTFRAGAFDQLK-------------QNATKARIPFYGSYTEMDPVIIAS 173G+ + DTFRAGA +QL+ ++ + + + D IA

Sbjct: 442 ENGFSVLIAACDTFRAGAVEQLRTHTRRLTALHPPEKHGGRTMVQLFEKGYGKDAAGIAM 501

High Scoring Pair (HSP)

7

BLASTP 2.2.9 [May-01-2004]

Reference: Altschul, Stephen F., Thomas L. Madden, Alejandro A. Schaffer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs", Nucleic Acids Res. 25:3389-3402.

Query= lcl|SRP54_MOUSE (P14576) Signal recognition particle 54 kDa protein (SRP54)(504 letters)

Database: swissprot197,228 sequences; 71,501,181 total letters

Searching..................................................done

Score E

Sequences producing significant alignments: (bits) Value

SRP54_MOUSE (P14576) Signal recognition particle 54 kDa protein ... 959 0.0 SRP54_PONPY (Q5R4R6) Signal recognition particle 54 kDa protein ... 958 0.0 SRP54_MACFA (Q4R965) Signal recognition particle 54 kDa protein ... 958 0.0 SRP54_HUMAN (P61011) Signal recognition particle 54 kDa protein ... 958 0.0 SRP54_CANFA (P61010) Signal recognition particle 54 kDa protein ... 958 0.0 SRP54_RAT (Q6AYB5) Signal recognition particle 54 kDa protein (S... 957 0.0 SRP54_GEOCY (Q8MZJ6) Signal recognition particle 54 kDa protein ... 794 0.0 SR542_LYCES (P49972) Signal recognition particle 54 kDa protein ... 565 e-161SR543_ARATH (P49967) Signal recognition particle 54 kDa protein ... 560 e-159SR542_HORVU (P49969) Signal recognition particle 54 kDa protein ... 558 e-158......SRPR_MOUSE (Q9DBG7) Signal recognition particle receptor alpha s... 99 3e-20SRPR_HUMAN (P08240) Signal recognition particle receptor alpha s... 99 3e-20SRPR_YEAST (P32916) Signal recognition particle receptor alpha s... 98 7e-20

BLAST output – revealing orthologs and paralogs

orthologs

paralogs

Genes or proteins are homologous if they are related by divergence from a common ancestor.

Orthology Sequences that diverged after a speciation event.Orthologous genes often have the samefunction in different species.

Paralogy Sequences that diverged after a gene duplicationevent.Paralogous genes perform different but related functions within one organism.

The two kinds of protein evolutionary relationship

X

X

X1

X

X2

Speciation

Orthologs

Ancestral organism

Organism A

Organism A

Organism B

Organism B

Orthologs

X

X

Xa

X

Xb

Gene duplication

Paralogs

Paralogs

8

Mouse trypsin -- orthologs -- Human trypsin| |paralogs paralogs| |Mouse chymotrypsin -- orthologs -- Human chymotrypsin

Example of orthology / paralogy relationships

Query Database

blastp Protein Proteinblastn DNA DNAtblastn Protein DNAblastx DNA Proteintblastx DNA DNA

The different variants of BLASTThe variants of BLAST

Cited 31998 times since 1990 !

BLAT

Alignment software specialized for next-generation sequencing technology

BTW BowtieSOAP2

Align reads to a reference genome

Reference genome

When BLAST is too slow:

9

Further improvement of computational efficiency - BLAT

(http://genome.ucsc.edu/cgi-bin/hgBlat?command=start)

Cited 34,646 times !

BLAST - searches in databases for sequence similarityClustalW - multiple alignment of sequences

Frequently used methods in sequence analysis that are based on sequence alignment

ClustalW

• Construction of tree based on pairwise alignments• Progressive alignment guided by tree.

AB

CD

E

HIV

Introduction to the practical“Examining HIV genes and proteins"

10

Introduction to the practical“Examining HIV genes and proteins"

EMBOSS programs in this practical

sixpackplotorf

dottup - dotplot analysiswater - Smith Waterman local alignmentneedle - Needleman - Wunsch global alignment

Introduction to the practical“Examining HIV genes and proteins"

11

M A K R K L K K N L K T F V A F S A I T F1W Q R E S * K R T * K L L L H L V L L L F2G K E K V K K E L K N F C C I * C Y Y C F3

1 ATGGCAAAGAGAAAGTTAAAAAAGAACTTAAAAACTTTTGTTGCATTTAGTGCTATTACT 60----:----|----:----|----:----|----:----|----:----|----:----|

1 TACCGTTTCTCTTTCAATTTTTTCTTGAATTTTTGAAAACAACGTAAATCACGATAATGA 60X A F L F N F F F K F V K T A N L A I V F6

X P L S F T L F S S L F K Q Q M * H * * F5H C L S L * F L V * F S K N C K T S N S F4

A L L L T N G I P I S A L T Q S S N T T F1L Y C * L M V F Q L V L * L S L P I Q L F2F I V N * W Y S N * C F N S V F Q Y N * F3

61 GCTTTATTGTTAACTAATGGTATTCCAATTAGTGCTTTAACTCAGTCTTCCAATACAACT 120----:----|----:----|----:----|----:----|----:----|----:----|

61 CGAAATAACAATTGATTACCATAAGGTTAATCACGAAATTGAGTCAGAAGGTTATGTTGA 120A K N N V L P I G I L A K V * D E L V V F6

Q K I T L * H Y E L * H K L E T K W Y L F5S * Q * S I T N W N T S * S L R G I C S F4

E I T S Q A T T G L R N V M Y Y G D W S F1R L L H K L L Q G Y V M * C I M V T G L F2D Y F T S Y Y R V T * C N V L W * L V Y F3

121 GAGATTACTTCACAAGCTACTACAGGGTTACGTAATGTAATGTATTATGGTGACTGGTCT 180----:----|----:----|----:----|----:----|----:----|----:----|

121 CTCTAATGAAGTGTTCGATGATGTCCCAATGCATTACATTACATAATACCACTGACCAGA 180S I V E C A V V P N R L T I Y * P S Q D F6

Q S * K V L * * L T V Y H L T N H H S T F5L N S * L S S C P * T I Y H I I T V P R F4

Translation of a nucleotide sequence using ‘sixpack’Introduction to the practical

“Examining HIV genes and proteins"

Plotorf to show open reading frames(in this case ORF is defined as starting with AUG codon)

Ribosomal protein S16 1771-2019

Ribosomal protein L19 3426-3773

Unnamed protein 416-1522 tRNA methyltransferase 2617-3384

Introduction to the practical“Examining HIV genes and proteins"

12

Gag

Gag-Pol fusion(5%)

Introduction to the practical“Examining HIV genes and proteins"

Introduction to the practical“Examining HIV genes and proteins"

Global alignment of mRNA sequence to genomic DNA sequence

Effect of gap parameters

mature, spliced mRNA

genomic DNA

Global alignment of mRNA sequence to genomic DNA sequence

Effect of gap parameters

13

Introduction to the practical“Examining HIV genes and proteins"

Dot plot analysis (dottup) reveals repeats

Introduction to the "Exercises with biological sequences -examining HIV genes and proteins"

- biological questions addressed with BLAST and ClustalX.

BLAST - search databases for sequence similarity

* Identifying homologous proteins. * Non-viral homologues to any HIV proteins?* Are we able to identify a relationship between human HIV

and the monkey SIV?

ClustalX - multiple sequence alignment

* Identifying amino acids involved in drug resistance.* What is the relationship between HIV and monkey SIV?* Using a multiple alignment to compute a phylogenetic tree.