The Basic Local Alignment Search Tool (BLAST)

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The Basic Local Alignment Search Tool (BLAST)

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Needleman-Wunsch coring scheme can be generalized from pair-wise to multiple alignment ... Order of pair-wise profile alignments determined ... – PowerPoint PPT presentation

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Title: The Basic Local Alignment Search Tool (BLAST)

1
The Basic Local Alignment Search Tool(BLAST)

Rapid data base search tool (1990)
Idea
(1) Search for high scoring segment pairs

2
The Basic Local Alignment Search Tool(BLAST)

A Y W T Y I V A L T Q V R Q Y E A T
S I L C I V M I Y S R A - Q Y R Y W R Y
Most local alignments contain highly conserved
sections without gaps

3
The Basic Local Alignment Search Tool(BLAST)

A Y W T Y I V A L T Q V R Q Y E A T
S I L C I V M I Y S R A - Q Y R Y W R Y
-gt search for high scoring segment pairs
(HSP), i.e. gap-free local alignments

4
The Basic Local Alignment Search Tool(BLAST)
5
The Basic Local Alignment Search Tool(BLAST)

A Y W T Y I V A L T Q V R Q Y E A T
S I L C I V M I Y S R A - Q Y R Y W R Y
Advantages
(a) speed
(b) statistical theory about HSP exists.

6
The Basic Local Alignment Search Tool(BLAST)

Rapid data base search tool (1990)
Idea
(1) Search for high scoring segment pairs
(2) Use word pairs as seeds

7
Pair-wise sequence alignment

T W L M H C A Q Y I
C
I
M X
H X
C X
T
H
Y
(1) Search word pairs of length 3 with score gt T,
Use them as seeds.

8
Pair-wise sequence alignment

Naïve algorithm would have a complexity of O(l1
l2)
Solution
Preprocess query sequence
Compile a list of all words that have a
Score gt T when aligned to a word in the
Query.

9
Pair-wise sequence alignment

Naïve algorithm would have a complexity of O(l1
l2)
Solution
Preprocess query sequence
Compile a list of all words that have a
Score gt T when aligned to a word in the
Query. Complexity O(l1)
Organize words in efficient data structure (tree)
for fast look-up

10
The Basic Local Alignment Search Tool(BLAST)

Rapid data base search tool (1990)
Idea
(1) Search for high scoring segment pairs
(2) Use word pairs as seeds
(3) Extend seed alignments until score drops
below threshold value

11
Pair-wise sequence alignment

T W L M H C A Q Y I
C
I
M X
H X
C X
T
H
Y
Extend seeds until score drops by X.

12
Pair-wise sequence alignment

T W L M H C A Q Y I
C
I X
M X
H X
C X
T X
H X
Y
Extend seeds until score drops by X.

13
Pair-wise sequence alignment

Algorithm not guaranteed to find best
segment pair
(Heuristic)
But works well in practice!

14
The Basic Local Alignment Search Tool(BLAST)

New BLAST version (1997)
Two-hit strategy

15
Pair-wise sequence alignment

W L M H C A Q Y A R V
I
M X
H X
C X
T
H
W
A X
R X
v X
Search two word pairs of at the same diagonal,
use lower threshold T

16
The Basic Local Alignment Search Tool(BLAST)

New BLAST version (1997)
Two-hit strategy
Gapped BLAST
Position-Specific Iterative BLAST
(PSI BLAST)

17
The Basic Local Alignment Search Tool(BLAST)

18
Multiple sequence alignment

1aboA 1 .NLFVALYDfvasgdntlsitkGEKLRVLgynhn
..............gE
1ycsB 1 kGVIYALWDyepqnddelpmkeGDCMTIIhrede
............deiE
1pht 1 gYQYRALYDykkereedidlhlGDILTVNkgslv
algfsdgqearpeeiG
1ihvA 1 .NFRVYYRDsrd......pvwkGPAKLLWkg...
..............eG
1vie 1 .drvrkksga.........awqGQIVGWYctnlt
.............peG
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP......
1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp
1ihvA 27 AVVIQd..nsdiKVVPRRKAKIIRd.....
1vie 28 YAVESeahpgsvQIYPVAALERIN......

19
Multiple sequence alignment

First question how to score multiple
alignments?
Possible scoring scheme
Sum-of-pairs score

20
Multiple sequence alignment

Multiple alignment implies pairwise alignments
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP......
1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp
1ihvA 27 AVVIQd..nsdiKVVPRRKAKIIRd.....
1vie 28 YAVESeahpgsvQIYPVAALERIN......

21
Multiple sequence alignment

Multiple alignment implies pairwise alignments
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP......
1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp
1ihvA 27 AVVIQd..nsdiKVVPRRKAKIIRd.....
1vie 28 YAVESeahpgsvQIYPVAALERIN......

22
Multiple sequence alignment

Multiple alignment implies pairwise alignments
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP......

23
Multiple sequence alignment

Multiple alignment implies pairwise alignments
1aboA 36 WCEAQtkngqGWVPSNYITPVN
1ycsB 39 WWWARlndkeGYVPRNLLGLYP

24
Multiple sequence alignment

Multiple alignment implies pairwise alignments
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP......
1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp
1ihvA 27 AVVIQd..nsdiKVVPRRKAKIIRd.....
1vie 28 YAVESeahpgsvQIYPVAALERIN......

25
Multiple sequence alignment

Multiple alignment implies pairwise alignments
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP......
1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp
1ihvA 27 AVVIQd..nsdiKVVPRRKAKIIRd.....
1vie 28 YAVESeahpgsvQIYPVAALERIN......

26
Multiple sequence alignment

Multiple alignment implies pairwise alignments
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp

27
Multiple sequence alignment

Multiple alignment implies pairwise alignments
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp

28
Multiple sequence alignment

Multiple alignment implies pairwise alignments
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP......
1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp
1ihvA 27 AVVIQd..nsdiKVVPRRKAKIIRd.....
1vie 28 YAVESeahpgsvQIYPVAALERIN......

29
Multiple sequence alignment

Multiple alignment implies pairwise alignments
Use sum of scores of these p.a.
1aboA 36 WCEAQt..kngqGWVPSNYITPVN......
1ycsB 39 WWWARl..ndkeGYVPRNLLGLYP......
1pht 51 WLNGYnettgerGDFPGTYVEYIGrkkisp
1ihvA 27 AVVIQd..nsdiKVVPRRKAKIIRd.....
1vie 28 YAVESeahpgsvQIYPVAALERIN......

30
Multiple sequence alignment

Goal
Find multi-alignment with maximum score !

31
Multiple sequence alignment

Needleman-Wunsch coring scheme can be generalized
from pair-wise to multiple alignment
Multidimensional search space instead of
two-dimensional matrix!

32
Multiple sequence alignment

33
Multiple sequence alignment

Complexity
For sequences of length l1 l2 l3
O( l1 l2 l3 )
For n sequences ( average length l )
O( ln )
Exponential complexity!

34
Multiple sequence alignment

Needleman-Wunsch coring scheme can be generalized
from pair-wise to multiple alignment
Optimal solution not feasible

35
Multiple sequence alignment

Needleman-Wunsch coring scheme can be generalized
from pair-wise to multiple alignment
Optimal solution not feasible
-gt Heuristics necessary

36
Multiple sequence alignment

(A) Carillo and Lipman (MSA)
Find sub-space in dynamic-programming
Matrix where optimal path can be found

37
Multiple sequence alignment

(B) Stoye, Dress (DCA)
Divide search space into small
Calculate optimal alignment for sub-spaces
Concatenate sub-alignments

38
Multiple sequence alignment

(B) Stoye, Dress (DCA)

39
Multiple sequence alignment

(B) Stoye, Dress (DCA)

40
Multiple sequence alignment

Progressive alignment.
Carry out a series of pair-wise alignment

41
Multiple sequence alignment

Most popular way of constructing multiple
alignments
Progressive alignment.
Carry out a series of pair-wise alignment

42

Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN
WWRLNDKEGYVPRNLLGLYP
AVVIQDNSDIKVVPKAKIIRD
YAVESEAHPGSFQPVAALERIN
WLNYNETTGERGDFPGTYVEYIGRKKISP

43
Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN
WWRLNDKEGYVPRNLLGLYP
AVVIQDNSDIKVVPKAKIIRD
YAVESEAHPGSFQPVAALERIN
WLNYNETTGERGDFPGTYVEYIGRKKISP
Align most similar sequences

44
Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN
WW--RLNDKEGYVPRNLLGLYP-
AVVIQDNSDIKVVP--KAKIIRD
YAVESEASFQPVAALERIN
WLNYNEERGDFPGTYVEYIGRKKISP

45
Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN
WW--RLNDKEGYVPRNLLGLYP-
AVVIQDNSDIKVVP--KAKIIRD
YAVESEASVQ--PVAALERIN------
WLN-YNEERGDFPGTYVEYIGRKKISP

46
Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN
WW--RLNDKEGYVPRNLLGLYP-
AVVIQDNSDIKVVP--KAKIIRD
YAVESEASVQ--PVAALERIN------
WLN-YNEERGDFPGTYVEYIGRKKISP
Align sequence to alignment

47
Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN-
WW--RLNDKEGYVPRNLLGLYP-
AVVIQDNSDIKVVP--KAKIIRD
YAVESEASVQ--PVAALERIN------
WLN-YNEERGDFPGTYVEYIGRKKISP
Align alignment to alignment

48
Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN--------
WW--RLNDKEGYVPRNLLGLYP--------
AVVIQDNSDIKVVP--KAKIIRD-------
YAVESEA---SVQ--PVAALERIN------
WLN-YNE---ERGDFPGTYVEYIGRKKISP

49
Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN--------
WW--RLNDKEGYVPRNLLGLYP--------
AVVIQDNSDIKVVP--KAKIIRD-------
YAVESEA---SVQ--PVAALERIN------
WLN-YNE---ERGDFPGTYVEYIGRKKISP
Rule once a gap - always a gap

50
Multiple sequence alignment

Order of pair-wise profile alignments determined
by phylogenetic tree based on pair-wise
similarity
values (guide tree)

51
Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN
WWRLNDKEGYVPRNLLGLYP
AVVIQDNSDIKVVPKAKIIRD
YAVESEAHPGSFQPVAALERIN
WLNYNETTGERGDFPGTYVEYIGRKKISP

52
Multiple sequence alignment

WCEAQTKNGQGWVPSNYITPVN
WWRLNDKEGYVPRNLLGLYP
AVVIQDNSDIKVVPKAKIIRD
YAVESEAHPGSFQPVAALERIN
WLNYNETTGERGDFPGTYVEYIGRKKISP

53
Multiple sequence alignment

Problem simple guide tree determines multiple
alignment multiple alignment determines
phyolgeneitc analysis

54
Multiple sequence alignment

Implementations
Clustal W, PileUp, MultAlin

55
Local multiple alignment

M
M
56
Local multiple alignment

M
M
M
57
Local multiple alignment
M
M
M
M
M
M
58
Local multiple alignment

Find motifs contained in all sequences in data
set
Problem
motifs often present in only sub-families

59

Neither local nor global methods appliccable
60

Alignment possible if order conserved
61
The DIALIGN approach

62
The DIALIGN approach

Combination of local and global methods.

63
The DIALIGN approach

Combination of local and global methods.
Find local pair-wise similarities between input
sequences
(fragments)

64
The DIALIGN approach

Combination of local and global methods.
Find local pair-wise similarities between input
sequences
(fragments)
Compose alignments from fragments

65
The DIALIGN approach

Combination of local and global methods.
Find local pair-wise similarities between input
sequences
(fragments)
Compose alignments from fragments
Ignore non-related parts of the sequences

66
The DIALIGN approach

atctaatagttaaactcccccgtgcttagagatccaaac
cagtgcgtgtattactaacggttcaatcgcgcacatccgc

67
The DIALIGN approach

atctaatagttaaactcccccgtgcttagagatccaaac
cagtgcgtgtattactaacggttcaatcgcgcacatccgc

68
The DIALIGN approach

atctaatagttaaactcccccgtgcttagagatccaaac
cagtgcgtgtattactaacggttcaatcgcgcacatccgc

69
The DIALIGN approach

atctaatagttaaactcccccgtgcttagagatccaaac
cagtgcgtgtattactaacggttcaatcgcgcacatccgc

70
The DIALIGN approach

atctaatagttaaactcccccgtgcttagagatccaaac
cagtgcgtgtattactaacggttcaatcgcgcacatccgc
------atctaatagttaaaccccctcgtgcttag-------agatccaa
ac
cagtgcgtgtattactaac----------ggttcaatcgcgcacatccgc
--

71
The DIALIGN approach

atctaatagttaaactcccccgtgcttagagatccaaac
cagtgcgtgtattactaacggttcaatcgcgcacatccgc
------atctaatagttaaaccccctcgtgcttag-------agatccaa
ac
cagtgcgtgtattactaac----------ggttcaatcgcgcacatccgc
--
------atcTAATAGTTAaaccccctcgtGCTTag-------AGATCCaa
ac
cagtgcgtgTATTACTAAc----------GGTTcaatcgcgcACATCCgc
--

72
The DIALIGN approach

Score of an alignment
Define score of fragment f
l(f) length of f
s(f) sum of matches (similarity values)
P(f) probability to find a fragment with length
l(f) and
at least s(f) matches in random
sequences that have
the same length as the input sequences.
Score w(f) -ln P(f)

73
The DIALIGN approach

Score of an alignment
Define score of alignment as
sum of scores w(f) of its fragments
No gap penalty is used!
Optimization problem for pair-wise alignment
Find chain of fragments with maximal total
score

74
The DIALIGN approach

------atctaatagttaaaccccctcgtgcttag-------agatccaa
ac
cagtgcgtgtattactaac----------ggttcaatcgcgcacatccgc
--
Fragment-chaining algorithm finds optimal chain
of
fragments.

75
The DIALIGN approach

Multiple fragment alignment
atctaatagttaaactcccccgtgcttag
cagtgcgtgtattactaacggttcaatcgcg
caaagagtatcacccctgaattgaataa

76
The DIALIGN approach

Multiple fragment alignment
atctaatagttaaactcccccgtgcttag
cagtgcgtgtattactaacggttcaatcgcg
caaagagtatcacccctgaattgaataa

77
The DIALIGN approach

Multiple fragment alignment
atctaatagttaaactcccccgtgcttag
cagtgcgtgtattactaacggttcaatcgcg
caaagagtatcacccctgaattgaataa

78
The DIALIGN approach

Multiple fragment alignment
atctaatagttaaactcccccgtgcttag
cagtgcgtgtattactaacggttcaatcgcg
caaagagtatcacccctgaattgaataa

79
The DIALIGN approach

Multiple fragment alignment
atctaatagttaaactcccccgtgcttag
cagtgcgtgtattactaacggttcaatcgcg
caaagagtatcacccctgaattgaataa

80
The DIALIGN approach

Multiple fragment alignment
atctaatagttaaactcccccgtgcttag
cagtgcgtgtattactaacggttcaatcgcg
caaagagtatcacccctgaattgaataa

81
The DIALIGN approach

Multiple fragment alignment
atc------taatagttaaactcccccgtgcttag
cagtgcgtgtattactaacggttcaatcgcg
caaagagtatcacccctgaattgaataa

82
The DIALIGN approach

Multiple fragment alignment
atc------taatagttaaactcccccgtgcttag
cagtgcgtgtattactaacggttcaatcgcg
caaa--gagtatcacccctgaattgaataa

83
The DIALIGN approach

Multiple fragment alignment
atc------taatagttaaactcccccgtgcttag
cagtgcgtgtattactaac----------ggttcaatcgcg
caaa--gagtatcacc----------cctgaattgaataa

84
The DIALIGN approach

Multiple fragment alignment
atc------taatagttaaactcccccgtgc-ttag
cagtgcgtgtattactaac----------gg-ttcaatcgcg
caaa--gagtatcacc----------cctgaattgaataa

85
The DIALIGN approach

Multiple fragment alignment
atc------taatagttaaactcccccgtgc-ttag
cagtgcgtgtattactaac----------gg-ttcaatcgcg
caaa--gagtatcacc----------cctgaattgaataa
Consistency it is possible to introduce gaps
such that all segment pairs are aligned.

86
The DIALIGN approach

Multiple fragment alignment
atc------TAATAGTTAaactccccCGTGC-TTag
cagtgcGTGTATTACTAAc----------GG-TTCAATcgcg
caaa--GAGTATCAcc----------CCTGaaTTGAATaa

87
Program evaluation

Use biologically verified alignments
(known 3D structure of proteins)
Compare alignments produced by
computer programs to biologically correct
alignments.

88
Program evaluation

(1) First evaluation of multiple alignment
programs (McClure, Vasi, Fitch,1994)
4 protein families used
Globin, kinase, protease, ribonuclease H,
all globally related -gt global programs
performed best

89
Program evaluation

(2) The BAliBASE (Thompson et al., 1999)
100 protein families with known 3D structure,
some with large insertions/deletions.

90
Program evaluation

1aboA 1 .NLFVALYDfvasgdntlsitkGEKLRVLgynhn
..............gE 1ycsB 1
kGVIYALWDyepqnddelpmkeGDCMTIIhrede............deiE
1pht 1 gYQYRALYDykkereedidlhlGDILTVNkgs
lvalgfsdgqearpeeiG 1ihvA 1
.NFRVYYRDsrd......pvwkGPAKLLWkg.................eG
1vie 1 .drvrkksga.........awqGQIVGWYctn
lt.............peG 1aboA 36
WCEAQt..kngqGWVPSNYITPVN...... 1ycsB 39
WWWARl..ndkeGYVPRNLLGLYP...... 1pht 51
WLNGYnettgerGDFPGTYVEYIGrkkisp 1ihvA 27
AVVIQd..nsdiKVVPRRKAKIIRd..... 1vie 28
YAVESeahpgsvQIYPVAALERIN...... Key alpha
helix RED beta strand GREEN core blocks
UNDERSCORE
91
Program evaluation

Results Four programs performed best, but no
method was best in all test examples. ClustalW,
SAGA and RPPR best for global alignment, DIALIGN
best for sequences with large insertions
or deletions.
92
Program evaluation

(3) Lassmann and Sonnhammer (2002) Used BAliBASE
plus artificial sequences for local
alignment Results T-COFFEE best for closely
related sequences, DIALIGN best for distal
sequences.
93
Program evaluation
94
Alignment of large genomic sequences

Important tool for identifying functional
sites (e.g. genes or regulatory elements)

95
Alignment of large genomic sequences

Phylogenetic Footprinting
Functional sites more conserved during evolution
gt Sequence similarity indicates biological
function

96
Alignment of large genomic sequences

DIALIGN performs well in identifying local
homologies, but is slow

97
Quadratic program running time

98
Quadratic program running time

99
Quadratic program running time

100
Quadratic program running time

101
Quadratic program running time

102
Quadratic program running time

103
Quadratic program running time

104
Solution Anchored alignments

105
Solution Anchored alignments

106
Solution Anchored alignments

107
Solution Anchored alignments

108
Solution Anchored alignments

109
Solution Anchored alignments

110
Solution Anchored alignments

111
Solution Anchored alignments

Find anchor points to reduce search space
112
Solution Anchored alignments

Use fast heuristic method to find anchor points
CHAOS developed together with Mike Brudno
Brudno et al. (2003), BMC Bioinformatics 466

113
Solution Anchored alignments

114
(3) Anchored alignments

115
(3) Anchored alignments

116
First step to gene predictionExon discovery by
genomic alignment

117
First step to gene predictionExon discovery by
genomic alignment

Evaluation of different alignment programs
Compare local sequence similarity identified by
alignment programs to known exons
Morgenstern et al. (2002), Bioinformatics
18777-787

118
DIALIGN alignment of human and murine genomic
sequences
119
DIALIGN alignment of tomato and Thaliana genomic
sequences
120

Evaluation of DIALIGN, PipMaker, WABA, BLASTN and
TBLASTX on a set of 42 human and murine genomic
sequences.
Compare similarities to annotated exons
Apply cut-off parameter to resulting alignments
Measure sensitivity and specificity

121
Performance of long-range alignment programs for
exon discovery (human - mouse comparison)
122
Performance of long-range alignment programs for
exon discovery (thaliana - tomato comparison)
123
AGenDA Alignment-based Gene Detection Algorithm

Bridge small gaps between DIALIGN fragments
-gt cluster of fragments
Search conserved splice sites and start/stop
codons at cluster boundaries to Identify
candidate exons
Recursive algorithm finds biologically consistent
chain of potential exons

124
Identification of candidate exons

Fragments in DIALIGN alignment
125
Identification of candidate exons

Build cluster of fragments
126
Identification of candidate exons

Identify conserved splice sites
127
Identification of candidate exons

Candidate exons bounded by conserved splice sites

128
Construct gene models using candidate exons

Score of candidate exon (E) based on DIALIGN
scores for fragments, score of splice junctions
and penalty for shortening / extending
Find biologically consistent chain of candidate
exons (starting with start codon, ending with
stop codon, no internal stop codons ) with
maximal total score

129
Find optimal consistent chain of candidate exons

130
Find optimal consistent chain of candidate exons

131
Find optimal consistent chain of candidate exons

132
Find optimal consistent chain of candidate exons

133
Find optimal consistent chain of candidate exons

atg
gt
ag
gt
ag
tga
atg
tga

134
Find optimal consistent chain of candidate exons

atg
gt
ag
gt
ag
tga
atg
tga
G1
G2

135
Find optimal consistent chain of candidate exons

Recursive algorithm calculates optimal chain of
candidate exons in N log N time

136
DIALIGN fragments
137
Candidate exons
138
Complete model
139
Results105 pairs of genomic sequences from
human and mouse (Batzoglou et al., 2000)
140
Results105 pairs of genomic sequences from
human and mouse (Batzoglou et al., 2000)