Bioinformatics Sequence comparison 2people.brunel.ac.uk/.../website_bioinformaticsHM/... · Global...

Bioinformatics

David GilbertBioinformatics Research Centre

www.brc.dcs.gla.ac.ukDepartment of Computing Science, University of Glasgow

Sequence comparison 2local pairwise alignment

(c) David Gilbert 2008 Sequence Comparison (2) 2

Lecture contents• Variations on dynamic programming

– Gap penalties– Substitution matrices

• To explain the reason that local alignments may be moreappropriate than global ones.

• To describe the use of Dot-Plots in visualising analignment

• To describe the Smith-Waterman method of finding andscoring an optimal local pairwise alignment

• To describe in outline the BLAST algorithm for databasesearch


Solution to Week 1 Exercise

A

A

D

C

0

ACEEA0


Percentage sequence identity number of identical residues x 100 = ________________________________ number of residues in smallest sequence

Can differ if have gaps/no_gaps: compute for these sequences:

-TGCAT-A- | | | |AT-C-TGAT

TGCATA | |ATCTGAT

Sequence similarity - change at amino-acid residue or nucleotide that preserves the physico-chemical properties of the residue


β and α globin, without gapsβ MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKα VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGK

β VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGα KVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPA

β KEFTPPVQAAYQKVVAGVANALAHKYHα VHASLDKFLASVSTVLTSKYR

Compute the identity%


β and α globin, with gapsCLUSTAL W (1.81) multiple sequence alignment

β MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKα --VLSPADKTNVKAAWGKVGAHAG----EYGAEALERMFLSFPTTKTYFPHFDLSHGSAQ β VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGα VKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLP

β KEFTPPVQAAYQKVVAGVANALAHKYHα AEFTPAVHASLDKFLASVSTVLTSKYR



Human beta globin hits coyote!

>SW:HBB_CANFA P02056 HEMOGLOBIN BETA CHAIN. Length = 146

Score = 276 bits (698), Expect = 2e-74 Identities = 131/146 (89%), Positives = 137/146 (93%)

Query:2 VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKV 61 VHLT EEKS V+ LWGKVNVDEVGGEALGRLL+VYPWTQRFF+SFGDLSTPDAVM N KVSbjct: 1 VHLTAEEKSLVSGLWGKVNVDEVGGEALGRLLIVYPWTQRFFDSFGDLSTPDAVMSNAKV 60

Query: 62 KAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGK 121 KAHGKKVL +FSDGL +LDNLKGTFA LSELHCDKLHVDPENF+LLGNVLVCVLAHHFGKSbjct: 61 KAHGKKVLNSFSDGLKNLDNLKGTFAKLSELHCDKLHVDPENFKLLGNVLVCVLAHHFGK 120

Query: 122 EFTPPVQAAYQKVVAGVANALAHKYH 147 EFTP VQAAYQKVVAGVANALAHKYHSbjct: 121 EFTPQVQAAYQKVVAGVANALAHKYH 146

Blast output


What happened?


Penalising gaps• Gap = maximal consecutive run of spaces in an alignment (1 or more

spaces)

• Simple penalty - each gap contributes a constant weight

• More complex - gap penalty proportional to gap length

• Large gap penalty → few gaps (less substrings in alignment). Smallpenalty → fragmented alignments.

• FASTA:– GAPOPEN: Penalty for the first residue in a gap

(-12 for proteins, -16 for DNA).

– GAPEXT: Penalty for additional residues in a gap(-2 for proteins, -4 for DNA).


Substitution matrices• Unitary matrix: match=1, mismatch=0

– sparse matrix (most elements are 0)• Poor diagnostic power

– all identical matches carry identical weighting• We can enhance scoring potential of weak but

biologically significant signals• Scoring matrices - weight matches for non-identical

residues according to observed substitution rates.• More on this later!

A C G T

A

C

G

T


Global and local alignment• Global alignment - as per dynamic programming

solution as explained– Needleman & Wunsch algorithm (1970)

• Local alignment - find local regions from eachstring which are similar:– Corresponds to shorter, localised paths in the matrix.– Justification - biological functional sites localised to

short conserved regions (no indels/mutations).– Smith-Waterman algorithm (1981)


Local alignment• Start & end dynamic programming computation at any cells instead of

(0,0) and (i,j)

• The matrix contains a maximum value that may not be at (i,j)[the end of the input sequences]– represents the endpoint of an alignment s.t. no other pair of segments with greater

similarity exists between the 2 sequences


Global vs local alignment

Global,Needleman &Wunsch

Local,Smith &Waterman


Local Pairwise Alignment• Distantly related sequences i.e. proteins

– Uneven accumulation of mutations along sequence• Similar segments in overall dissimilar sequences

– Rearrangement of gene segments in genome• Related sub-sequences in unrelated genes

• Local similarity corresponds to– Shared structural or functional motif

• Robust to mutations• Evolutionarily important

• Global alignment may fail in such cases– Island of similarity lost in random symbol matches


Local Pairwise Alignment

• Require to find similar segments in sequences

• Database search task : Find homologous sequences {d} to query q indatabase D– In a reasonable time– Present only homologous sequences (True Positives)– Do not present non-homologous sequences (False Positives)

• First – how to find local alignments?


Dot Matrices• First technique to discover local similarities

– M by N matrix created – symbols of q (length M) on one axis, symbols of d(length N) on the other

– Matrix populated with dots and spaces– Dot in cell (i,j) indicates that q(i) = d(j)

• Easy to understand visualisation

• Common substrings found easily – contiguous diagonal dots


Dot plots• A convenient way of comparing 2 sequences visually

• Use matrix, put 1 sequence on X-axis, 1 on Y-axis

• Cells with identical characters filled with a ‘1’, non-identical with ‘0’ (simplest scheme -could have weights)

• Identical sequences will look like WHAT?

• Similar sequences will have a broken diagonal, plus some other lines

• Distantly related sequences - much noisier.

• Can generate an alignment

• Best path through dotplot given by dynamic programming algorithms:– global alignment = Needleman & Wunsch– local alignment = Smith & Waterman


H L T P E E K S V H T

H

A

K

P

E

E

K

S

A

V

T

Dot plots


H L T P E E K S V H T

H x x

A

K x

P x

E x x

E x x

K x

S x

A

V x

T x x

Dot plots

AlignmentHLTPEEKSVHT| ||||| |HAKPEEKSAVT


A dotplot


Try a dotplot and alignmentM T F R D L L S V S F E G P R P

M

T

F

R

D

L

L

S

V

S

F

E

G

P

R

P

D

S

S

A

G

G


Try a dotplot and alignment

• Two sequences

q = ANTGDSCTAWCDEFGHIKPQWERTY

d = TREDFGAACDEFGHIKLHYTYTRTRERAECDEFGHIKHYGT


Dot Matrices• Easy to identify common recurring substring

CDEFGHIK

• Anti-diagonal identifies reversed substring TRE

• Matrix image can be ‘noisy’– Most of dots not associated with a common substring

• Matrices can be very large & unwieldy for typical proteinsequences ~ 500 ~ 1000 aa’s


Smith-Waterman Method• Require objective score of alignment

• Can employ dynamic programming method (Lecture 1) thoughrequires some changes

• Consider following two sequences– q = ACEDECADE– d = REDCEDKL

• Unsure at what symbols (residues) highest scoring local alignmentsend – all pairs should be considered

• Consider q8 and d6


Smith-Waterman Method• Consider q8 and d6 i.e q = ACEDECAD & d = REDCED• Scoring 0.5 equality, -0.3 inequality, -0.5 gap

• Removing first two pairs in alignment will improve alignment score – negative scores

0.4-0.10.2-0.30.2-0.3-0.8-0.3a.s0.5-0.30.5-0.50.50.5-0.5-0.3c.sDEC-DE-Rd6

DACEDECAq8


Smith-Waterman Method

1.20.71.00.51.00.5a.s

0.5-0.30.5-0.50.50.5c.s

DEC-DEd2…6

DACEDEq3…8


Smith-Waterman Method• Removing prefixes with negative scores improves overall score

• To find best local alignment ending in qi & dj must find beststarting point

• Fortunately a DP method can be employed

• In this case all negative values are replaced with zero

• Simple change to the global alignment DP



Si-1,j-1 + s(qi, dj)

Si,j-1 + s(-, dj)Si,j = max

Si-1,j + s(qi, -)0{


Smith-Waterman Method• Now initialise first row & columns with 0• In this example: Score as 0.5 equality, -0.3 inequality, -0.5 gap, Si,j ≥ 0

0.40.90.70.50.200.500E

0.20.71.20.200.5000D

0.40.40.20.70.50000A

0.90.70.70.510.2000C

0.71.21.01.00.70.50.500E

0.511.50.50.51000D000.51000.500E00000.50000C000000000A

0000000000

LKDECDER0I/J



• Initialise first row & columns with 0

• Si,j ≥ 0

• Find maximum partial alignment score Sk,l

• Trace backwards, constructing alignment, until reach acell with value 0



0.40.90.70.50.200.500E

0.20.71.20.200.5000D

0.40.40.20.70.50000A

0.90.70.70.510.2000C

0.71.21.01.00.70.50.500E

0.511.50.50.51000D000.51000.500E00000.50000C000000000A

0000000000

LKDECDER0I/J


• Trace backwards, constructing alignment, until reach a cell with value 0


FASTA (Lipman & Pearson 1985)• Fast approximation to the Smith-Waterman algorithm

• Local alignments - tries to find paths of regional similarity, rather than trying to find thebest alignment between 2 sequences.

• Alignments can contain gaps.

• Rapid

• Heuristic - not guaranteed to find the best alignment between 2 sequences; it may missmatches.– uses a strategy which is expected to find most matches, but sacrifices complete sensitivity in

order to gain speed.

• A substitution matrix is used during all phases of protein searches


FASTA• Identifies short words (k-tuples) common to both sequences

(nucleotides k=1 or 2, amino-acids, k up to 6)

• Similar to focussing on diagonal matches in dynamicprogramming algorithm

• Uses heuristic to join k-tuples close on same diagonal

• If significant number of matches found, then uses dynamicprogramming to compute gapped alignment


FASTAalgorithm

www.compbio.dundee.ac.uk/ftp/preprints/review93/Figure9.pdf


FASTA outputHBB_CANFA P02056 HEMOGLOBIN BETA CHAIN. (146 aa)initn: 886 init1: 886 opt: 886 Z-score: 1095.3 bits: 208.6 E(): 2.9e-54Smith-Waterman score: 886; 89.726% identity (89.726% ungapped) in 146 aa overlap (2-147:1-146)

10 20 30 40 50 60EMBOSS MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK :::: :::: :..:::::::::::::::::::.:::::::::.::::::::::::.: :SW:HBB VHLTAEEKSLVSGLWGKVNVDEVGGEALGRLLIVYPWTQRFFDSFGDLSTPDAVMSNAK 10 20 30 40 50

70 80 90 100 110 120EMBOSS VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG :::::::::..::::: .::::::::: ::::::::::::::::.:::::::::::::::SW:HBB VKAHGKKVLNSFSDGLKNLDNLKGTFAKLSELHCDKLHVDPENFKLLGNVLVCVLAHHFG 60 70 80 90 100 110

130 140 EMBOSS KEFTPPVQAAYQKVVAGVANALAHKYH ::::: :::::::::::::::::::::SW:HBB KEFTPQVQAAYQKVVAGVANALAHKYH

120 130 140


Database Search - BLAST• SW complexity similar to DP for global alignment

• Not realistic for database search in terms of time

• Trade-off guarantee of finding best alignment with time expense

• Basic Local Alignment Search Tool – BLAST (Alschul et al 1990)

• Employs fast search to find small segments with similar score in both sequences

• Extend small segments (local alignments)

• Returns maximal scoring pairs (MSP) & MSP score & statistical significance ofscores


Database Search - BLAST

• Query sequence split into words of defined length– Query string q = AFGTULL with word length of L = 3– AFG, FGT, GTU, TUL, ULL

• Define a threshold alignment score T

• Find all word-pairs of length L with score ≥ T– For amino acids there are 203 = 8000 distinct words w of length L=3– e.g Find all w such that S(w, AFG) ≥ T



• Search database for all ‘hits’ - sequences with exact matches to each w– Indexing of sequences to create ‘inverted file’ by employing hash table to index

sequences – fast

• Extend alignment of ‘hits’ while score increases – producing High Scoring Pair’s

• Return sequences with HSP’s which have significantly (statistically) higherscores than a threshold Smax

• Smax obtained empirically from random sequences



• Varying the threshold alignment score T– Search time decreases as T is increased, fewer word pairs are found– Sensitivity of search decreases as T is increased, word pairs overlooked (homologous

sequences may be discarded)

• The score of the alignment Smax AND the associated statistical significance arerequired to assess whether homology is suggested


BLAST - Basic Local Alignment ToolAltschul et al 1990

• Given 2 sequences:– Segment pair - pair of subsequences of the same length forming an ungapped

alignment– Computes all segment pairs– If there is a MSP maximal segment pair (highest score of all pairs for 1

comparison) above some cutoff score C and C is “significant” then report hit– Also reports those sequences where the score of MSP < C, but several segment

pairs in combination which are significant.– Reports score from highest scoring pairs & probability scores [E values]

(expected by chance).

• Only produces ungapped alignments


BLASTAlgorithm

www.compbio.dundee.ac.uk/ftp/preprints/review93/Figure10.pdf


Gapped BLAST (Altschul et al 1997)

• Seeks only 1 (not all) ungapped alignments withsignificant match

• Uses dynamic programming to extend residuesin both directions to give gapped alignment

• 3x faster than ungapped BLAST


Edited results (EMBL)Database: embl: 958,670 sequences; 2,466,994,978 total letters Score ESequences producing significant alignments: (bits) Value

EM_HUM:HSBGL1 V00497 Human messenger RNA for beta-globin. 1241 0.0EM_HUM:AF181989 AF181989 Homo sapiens hemoglobin beta subuni... 1116 0.0EM_HUM:HSHEMOB M25113 Human sickle beta-hemoglobin mRNA. 1100 0.0EM_PAT:I32884 I32884 Sequence 9 from patent US 5589367. 910 0.0EM_HUM:HS202231 U20223 Human thalassemia beta globin gene, c... 416 e-114EM_OM:AGHBD M19061 Spider monkey (A.geoffroyi) delta-globin ... 369 1e-99EM_OM:CPHBB5CP J00330 monkey (c.polykomos) beta-globin gene;... 367 4e-99EM_OM:PPHBD M21825 Orangutan delta globin gene, complete cds. 347 4e-93EM_OM:CPHBDPSC J00335 Monkey (colobus) delta-globin pseudoge... 297 3e-78EM_OM:LMHBB M15734 Lemur (brown) beta-globin gene, complete ... 270 7e-70EM_OM:TSHBD J04428 T.syrichta delta-globin gene, complete cds. 266 1e-68EM_OM:OCU60902 U60902 Otolemur crassicaudatus epsilon-, gamm... 266 1e-68EM_OM:LEBGLOB Y00347 Lepus europaeus adult beta-globin gene 266 1e-68EM_OM:GCDELGLB M61740 G.crassicaudatus beta globin gene, com... 266 1e-68EM_OM:MOHBDPS J00332 monkey (anubis) silent delta-globin gene. 262 2e-67EM_OM:TSHBB J04429 T.syrichta beta globin gene, complete cds. 260 7e-67EM_PAT:A34698 A34698 Synthetic pSXBeta+ sequence 258 3e-66EM_OM:OCBGLO V00882 Rabbit (O. cuniculus) gene for beta-globin. 250 7e-64EM_OM:BTBG M63453 Bovine Beta globin gene and globin (PSI-3)... 220 6e-55


Edited results (Swiss-prot)Database: swissprot: 86,593 sequences; 31,411,157 total letters

Score ESequences producing significant alignments: (bits) Value

SW:HBB_HUMAN P02023 HEMOGLOBIN BETA CHAIN. (human) 306 2e-83SW:HBB_GORGO P02024 HEMOGLOBIN BETA CHAIN. (gorilla) 305 4e-83SW:HBB2_PANLE P18988 HEMOGLOBIN BETA-2 CHAIN. (lion) 302 3e-82SW:HBB_HYLLA P02025 HEMOGLOBIN BETA CHAIN. (gibbon) 300 8e-82SW:HBB_PREEN P02032 HEMOGLOBIN BETA CHAIN. (Hanumam langur) 298 5e-81SW:HBB_COLPO P19885 HEMOGLOBIN BETA CHAIN. (Colobus) 295 3e-80SW:HBB_CERAE P02028 HEMOGLOBIN BETA CHAIN. (Green monkey) 295 3e-80SW:HBB_MACFU P02027 HEMOGLOBIN BETA CHAIN. (Japanese macaque) 293 2e-79SW:HBB_CALAR P18985 HEMOGLOBIN BETA CHAIN. (Marmoset) 292 2e-79SW:HBB_ATEGE P02034 HEMOGLOBIN BETA CHAIN. (Spider monkey) 292 2e-79SW:HBB_MANSP P08259 HEMOGLOBIN BETA CHAIN. (Mandrill) 291 4e-79…SW:HBB1_RAT P02091 HEMOGLOBIN BETA CHAIN, (Rat) 255 4e-68SW:HBB_ERIEU P02059 HEMOGLOBIN BETA CHAIN. (Hedgehog) 252 2e-67SW:HBB_PANPO P04244 HEMOGLOBIN BETA CHAIN. (Bison) 251 5e-67SW:HBB_BISBO P09422 HEMOGLOBIN BETA CHAIN. (Leopard) 251 5e-67

Database: swissprot: 86,593 sequences; 31,411,157 total letters

Score ESequences producing significant alignments: (bits) Value

SW:HBB_HUMAN P02023 HEMOGLOBIN BETA CHAIN. (human) 306 2e-83SW:HBB_GORGO P02024 HEMOGLOBIN BETA CHAIN. (gorilla) 305 4e-83SW:HBB2_PANLE P18988 HEMOGLOBIN BETA-2 CHAIN. (lion) 302 3e-82SW:HBB_HYLLA P02025 HEMOGLOBIN BETA CHAIN. (gibbon) 300 8e-82SW:HBB_PREEN P02032 HEMOGLOBIN BETA CHAIN. (Hanumam langur) 298 5e-81SW:HBB_COLPO P19885 HEMOGLOBIN BETA CHAIN. (Colobus) 295 3e-80SW:HBB_CERAE P02028 HEMOGLOBIN BETA CHAIN. (Green monkey) 295 3e-80SW:HBB_MACFU P02027 HEMOGLOBIN BETA CHAIN. (Japanese macaque) 293 2e-79SW:HBB_CALAR P18985 HEMOGLOBIN BETA CHAIN. (Marmoset) 292 2e-79SW:HBB_ATEGE P02034 HEMOGLOBIN BETA CHAIN. (Spider monkey) 292 2e-79SW:HBB_MANSP P08259 HEMOGLOBIN BETA CHAIN. (Mandrill) 291 4e-79…SW:HBB1_RAT P02091 HEMOGLOBIN BETA CHAIN, (Rat) 255 4e-68SW:HBB_ERIEU P02059 HEMOGLOBIN BETA CHAIN. (Hedgehog) 252 2e-67SW:HBB_PANPO P04244 HEMOGLOBIN BETA CHAIN. (Bison) 251 5e-67SW:HBB_BISBO P09422 HEMOGLOBIN BETA CHAIN. (Leopard) 251 5e-67


Blast alignment

>SW:HBB_CANFA P02056 HEMOGLOBIN BETA CHAIN. Length = 146

Score = 276 bits (698), Expect = 2e-74 Identities = 131/146 (89%), Positives = 137/146 (93%)

Query: 2 VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKV 61 VHLT EEKS V+ LWGKVNVDEVGGEALGRLL+VYPWTQRFF+SFGDLSTPDAVM N KVSbjct: 1 VHLTAEEKSLVSGLWGKVNVDEVGGEALGRLLIVYPWTQRFFDSFGDLSTPDAVMSNAKV 60

Query: 62 KAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGK 121 KAHGKKVL +FSDGL +LDNLKGTFA LSELHCDKLHVDPENF+LLGNVLVCVLAHHFGKSbjct: 61 KAHGKKVLNSFSDGLKNLDNLKGTFAKLSELHCDKLHVDPENFKLLGNVLVCVLAHHFGK 120

Query: 122 EFTPPVQAAYQKVVAGVANALAHKYH 147 EFTP VQAAYQKVVAGVANALAHKYHSbjct: 121 EFTPQVQAAYQKVVAGVANALAHKYH 146

Blast output


FASTA vs BLAST• BLAST faster than FASTA without significant loss of ability to

find the similar database sequences.

• BLAST & FAST equivalent for highly similar sequences

• FASTA may be better for less similar sequences

• But can always make a full local alignment(Smith-Waterman algorithm) - highest potential of finding lesssimilar sequences in a database search since the entire sequencelengths are compared.


EMBL - some stats31/01/06

Total nucleotides(current 118,263,140,052)

Number of entries(current 65,933,089)


Smith-Waterman programs

• MPsrch Edinburgh University

– http://www.ebi.ac.uk/MPsrch/

• Scanps2.3 Geoff Barton (EBI; University of Dundee)

– http://www.ebi.ac.uk/scanps/

• Blitz (bic_sw) Compugen -- uses MPsrch & Scanps

– http://www.ebi.ac.uk/bic_sw/ (email only)


Multiple alignments• Analyse gene families

– reveal (subtle) conserved family characteristics

characters 1 2 3 4 5 6 7 8 9 10

S1 Y D G G A V - E A L S2 Y D G G - - - E A L S3 F E G G I L V E A L S4 F D - G I L V Q A V S5 Y E G G A V V Q A L

consensus y d G G AI VL V e A l

sequ

ence

s


• Simultaneous: N-wise alignment (adapted from pairwise approach)

– uses N-dimension matrix.– Complexity is

• O(m1m2) [2 sequences length m1 & m2 ]• O(mn) [n sequences of length m]

– Thus only good for short sequences.

• Manua1 (!)

• Progessive (heuristic) e.g. ClustalW:– compute pairwise sequence identities– construct binary tree (can output phylogenetic tree)– align similar sequences in pairs, add distantly related ones later.

Multiple aligment - methods

s1

s2

s3

s4

s5

a1

a2

a3

a4


Multiple sequence alignment (globins)CLUSTAL W (1.81) multiple sequence alignment

Human VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKV 60Gorilla VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKV 60Rabbit VHLSSEEKSAVTALWGKVNVEEVGGEALGRLLVVYPWTQRFFESFGDLSSANAVMNNPKV 60Pig VHLSAEEKEAVLGLWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSNADAVMGNPKV 60 ***:.***.** .*******:****************************..:***.****

Human KAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGK 120Gorilla KAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFKLLGNVLVCVLAHHFGK 120Rabbit KAHGKKVLAAFSEGLSHLDNLKGTFAKLSELHCDKLHVDPENFRLLGNVLVIVLSHHFGK 120Pig KAHGKKVLQSFSDGLKHLDNLKGTFAKLSELHCDQLHVDPENFRLLGNVIVVVLARRLGH 120 ******** :**:** **********.*******:********:*****:* **::::*:

Human EFTPPVQAAYQKVVAGVANALAHKYH 146Gorilla EFTPPVQAAYQKVVAGVANALAHKYH 146Rabbit EFTPQVQAAYQKVVAGVANALAHKYH 146Pig DFNPNVQAAFQKVVAGVANALAHKYH 146 :*.* ****:****************


Multiple sequencealignments &

phylogenetic trees

((Human:0.00000,Gorilla:0.00685) :0.04110,Rabbit:0.05479,Pig:0.10959);

Pair ScoreHuman-Gorilla 99Human-Rabbit 90Gorilla-Rabbit 89Human-Pig 84Gorilla-Pig 84Rabbit-Pig 83


What can we do with multiple alignments?• Create (databases of) profiles derived from multiple alignments for protein

families– profile = multiple alignment + observed character frequencies at each

position

• Search with a sequence against a database of profiles(e.g. PROSITE database)– faster than sequence against sequence– gives a more general result (“the input sequence matches globin profile”)

• Search with a profile against a database of sequences– PSI-BLAST : can identify more distant relationships than by normal

BLAST search


PSI-BLAST (position specific iterated BLAST)

Single proteinsequence

Search database(BLAST)

Multiple alignmentProfile

Estimate statisticalsignificance of local

alignments

?iterateuntil

convergence


PSI-BLAST (Altschul et al 1997)

(1) Start with 1 sequence (or profile) = ‘probe’

(2) Search with BLAST and select top hits manually orautomatically

(3) Make multiple alignment & profile

(4) Estimate statistical significance of local alignments.If significance ok & you want to continue, then go to (1) using theprofile, else exit


Dates &programs

FAST

A

BLA

ST

Gap

ped

BLA

ST&

PSI

BLA

ST

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