Homology Search Tools

Kun-Mao Chao (趙坤茂 )Department of Computer Science an

d Information EngineeringNational Taiwan University, Taiwan

WWW: http://www.csie.ntu.edu.tw/~kmchao

2

Homology Search Tools

• Smith-Waterman(Smith and Waterman, 1981; Waterman and Eggert, 1987)

• FASTA(Wilbur and Lipman, 1983; Lipman and Pearson, 1985)

• BLAST(Altschul et al., 1990; Altschul et al., 1997)

• BLAT(Kent, 2002)

• PatternHunter(Li et al., 2004)

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Finding Exact Word Matches

• Hash Tables

• Suffix Trees

• Suffix Arrays

4

Hash Tables

… …

… …

… …

… …

… …

… …

CATCCA

CTT

TCCTCGTCT

TTT

GAT

010011 (19)010100 (20)

011111 (31)

100011 (35)

110101 (53)

110111 (55)110110 (54)

111111 (63)

AAA000000 (0)

ATC001101 (13)

1

2

3

45

6

7

8

AG TTCTACCT

1021 9876543

5

Suffix Trees (I)

AG TTCTACCT

1021 9876543

10

362

8

4

519

ATC

CATCTT TT

GATCCATCTTC

CATCTT

TTATCTT

T

CATCTTTT

T

7

C

6

Suffix Trees (II)11

AG TTCTACCT

1021 9876543

$

10

362

8

4

5

19

ATC

CATCTT$ TT$

GATCCATCTT$C

CATCTT$

TT$ATCTT$

T

CATCTT$

TT$

T$

7

C

$

$

11

7

Suffix Arrays

AG TTCTACCT

1021 9876543 ATCCATCTT 2

ATCTT 6

CATCTT 5

CCATCTT 4

CTT 8

GATCCATCTT 1

T 10

TCCATCTT 3

TCTT 7

TT 9

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FASTA

1) Find runs of identities, and identify regions with the highest density of identities.

2) Re-score using PAM matrix, and keep top scoring segments.

3) Eliminate segments that are unlikely to be part of the alignment.

4) Optimize the alignment in a band.

9

FASTA

Step 1: Find runes of identities, and identify regions with the highest density of identities.

Sequence A

Sequence B

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FASTA

Step 2: Re-score using PAM matrix, andkeep top scoring segments.

11

FASTA

Step 3: Eliminate segments that are unlikely to be part

of the alignment.

12

FASTA

Step 4: Optimize the alignment in a band.

13

BLAST

Basic Local Alignment Search Tool(by Altschul, Gish, Miller, Myers and Lipman)

The central idea of the BLAST algorithm is that a statistically significant alignment is likely to contain a high-scoring pair of aligned words.

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The maximal segment pair measure

A maximal segment pair (MSP) is defined to be the highest scoring pair of identical length segments chosen from 2 sequences.(for DNA: Identities: +5; Mismatches: -4)

the highest scoring pair

•The MSP score may be computed in time proportional to the product of their lengths. (How?) An exact procedure is too time consuming.

•BLAST heuristically attempts to calculate the MSP score.

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A matrix of similarity scores

G

CTACCTA

TC

T

-4

GTCTTACTA-4-4-4-4-4-4-4 5-4-4

-4 -4-45-4-45-4 -4-4-4

-4 55-4-45-45 -45-4

5 -4-4-45-4-4-4 -4-45

5 -4-4-45-4-4-4 -4-45

-4 -4-45-4-45-4 -4-4-4

-4 55-4-45-45 -45-4

5 -4-4-45-4-4-4 -4-45

-4 55-4-45-45 -45-4

T -4 55-4-45-45 -45-4

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A maximum-scoring segment

10

1110

G

CTACCTA

TC

T

-4

GTCTTACTA-4-4-4-4-4-4-4 5-4-4

-4 -4-45-4-4-4 -4-4-4

-4 55-4-4-45 -45-4

5 -4-4-4-4-4-4 -4-45

5 -4-45-4-4-4 -4-45

-4 -45-4-45-4 -4-4-4

-4 5-4-45-45 -45-4

5 -4-4-45-4-4-4 -4-4

-4 55-4-45-45 -4-4

5

5

5

-4

-4

5

5

1

8765432

9

21 9876543

T -4 55-4-45-45 -45-4

5

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BLAST

1) Build the hash table for Sequence A.

2) Scan Sequence B for hits.

3) Extend hits.

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BLASTStep 1: Build the hash table for Sequence A. (3-tuple example)

For DNA sequences:

Seq. A = AGATCGAT 12345678AAAAAC..AGA 1..ATC 3..CGA 5..GAT 2 6..TCG 4..

TTT

For protein sequences:

Seq. A = ELVIS

Add xyz to the hash table if Score(xyz, ELV) T;≧Add xyz to the hash table if Score(xyz, LVI) T;≧Add xyz to the hash table if Score(xyz, VIS) T;≧

19

BLASTStep2: Scan sequence B for hits.

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BLASTStep2: Scan sequence B for hits.

Step 3: Extend hits.

hit

Terminate if the score of the sxtension fades away. (That is, when we reach a segment pair whose score falls a certain distance below the best score found for shorter extensions.)

BLAST 2.0 saves the time spent in extension, and

considers gapped alignments.

21

Gapped BLAST (I)D

The two-hit method

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Gapped BLAST (II)

Confining the dynamic-programming

HSP with score at least Sq

seed residue pair

region confined by Xq

23

BLAT

database

index

query

24

PatternHunter (I)

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PatternHunter (II) T

… …

… …

… …

… …

… …

… …

AG TTCTACC

1021 9876543

CAC

TCA

TCT

TTT

GAC

010001 (17)

100001 (33)

110100 (52)

110111 (55)

111111 (63)

AAA000000 (0)

ATC001101 (13)

1

2

3

4

5

7

ATT001111 (15) 6

… …010100 (20) CCT

ATG001110 (14)

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Remarks

• Filtering is based on the observation that a good alignment usually includes short identical or very similar fragments.

• The idea of filtration was used in FASTA, BLAST, BLAT, and PatternHunter.