Sequence Alignment

1
Sequence Alignment
BCB 444/544 - Introduction to Bioinformatics

• Lecture 8
• 8_Sept08

Slides adapted from D. Fernandez-Baca ISU
2
Assignments Reading Exercises(before lecture)

• vFri Sept 8
• CH Chp 2 pp 34-59
• Also, DQs MMs
• Re Sequence alignment Dynamic programming
• http//en.wikipedia.org/wiki/Sequence_alignment
• (read all except sections on Multiple sequence
  alignment, Structural alignment, Phylogenetic
  analysis)
• Mon Sept 11
• Re Predicting Protein Function
• Read Friedberg I, Harder T, Godzik A. (2006)
  JAFA a protein function annotation meta-server.
  Nucleic Acids Res. 34 (Web Server issue)W379-81
  PMID 16845030
• http//nar.oxfordjournals.org/cgi/content/full/34
  /suppl_2/W379
• Visit http//jafa.burnham.org/

3
Why compare sequences?

• To determine whether two (or more) genes or
  proteins are evolutionarily related to each other
• To identify structurally or functionally similar
  regions within proteins
• Other?

4
Sequence Comparison Methods

• Dot Matrix Analysis
• Dynamic Programming
• Word or k-tuple methods (BLAST and FASTA)

5
Dot matrices
c
g
g
a
c
a
c
a
c
g
6
Dot matrix comparison
7
Interpretation

• Regions of similarity appear as diagonal runs of
  dots
• Reverse diagonals (perpendicular to diagonal)
  indicate inversions
• Reverse diagonals crossing diagonals (Xs)
  indicate palindromes

8
Dynamic Programming
9
Pair-wise sequence alignments
Idea Display one sequence above another with
spaces inserted in both to reveal similarity

• A C A T - T C A - C
• B C - T C G C A G C

10
Two types of alignment
S CTGTCGCTGCACG T TGCCGTG
Global alignment
Local alignment
CTGTCG-CTGCACG -TGC-CG-TG----
CTGTCGCTGCACG-- -------TGC-CGTG
CTGTCG-CTGCACG -TGCCG--TG----
Is this a better alignment?
11
Global alignment Scoring
CTGTCG-CTGCACG -TGC-CG-TG----
Reward for matches ? Mismatch penalty ? Space
penalty ?
score(A) ?w ?x - ?y
w matches x mismatches y spaces
12
Global alignment Scoring
Reward for matches 10 Mismatch penalty
2 Space penalty 5
C T G T C G C T G C - T G C
C G T G -
-5 10 10 -2 -5 -2 -5 -5 10 10 -5
Total 11
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Optimum Alignment

• The score of an alignment is a measure of its
  quality
• Optimum alignment problem Given a pair of
  sequences X and Y, find an alignment (global or
  local) with maximum score

14
Alignment algorithms

• Global Needleman-Wunsch
• Local Smith-Waterman
• NW and SW use dynamic programming
• Variations
• Gap penalty functions
• Scoring matrices

15
Global Alignment Algorithm
16
Theorem. C(i,j) satisfies the following
relationships
Initial conditions
Recurrence relation For 1 ? i ? n, 1 ? j ? m
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Justification
18
Example
Case 1 Line up Si with Tj
i
i - 1
S C A T T C A C T C - T T C A
G
j
j -1
Case 2 Line up Si with space
i - 1
i
S C A T T C A - C T C - T T
C A G -
j
Case 3 Line up Tj with space
i
S C A T T C A C - T C - T T
C A - G
j
j -1
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Computation Procedure
C(0,0)
C(i,j)
C(n,m)
20
? C T C G C
A G C
0 -5 -10 -15 -20 -25 -30 -35 -40
?
10
5
C
A
T
T
C
A
C
10 for match, -2 for mismatch, -5 for space
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? C T C G C
A G C
?
C
A
T
T
C
A
C
Traceback can yield both optimum alignments
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Local Alignment Motivation

• Ignoring stretches of non-coding DNA
• Non-coding (or "non-functional") regions may be
  more likely to contain mutations than coding
  regions.
• Local alignment between two gene sequences is
  likely to be between two exons
• Locating protein domains
• Proteins of different kind and of different
  species often exhibit local similarities
• Local similarities may indicate functional
  subunits

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Local alignment Example
S g g t c t g a g T a a a c g a
Match 2 Mismatch and space -1
Best local alignment
g g t c t g a g a a a c g a -
Score 5
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Local Alignment Algorithm
C i, j Score of optimally aligning a
suffix of s with a suffix of t.

• Initialize top row and leftmost column to zero.

25
? C T C G C
A G C
?
C
A
T
T
C
A
C
1 for a match, -1 for a mismatch, -5 for a space
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Some Results

• Most pairwise sequence alignment problems can be
  solved in O(mn) time.
• Space requirement can be reduced to O(mn), while
  keeping run-time fixed Myers88.
• Highly similar sequences can be aligned in O(dn)
  time, where d measures the distance between the
  sequences Landau86.

27
Affine Gap Penalty Functions

• Gap penalty h gk
• where
• k length of gap
• h gap opening penalty
• g gap continuation penalty

Can also be solved in O(nm) time using dynamic
programming
28
Database Searches

• BLAST

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BLAST

• Basic Local Alignment Search Tool
• Altschul, Gish, Miller, Myers, Lipman, J. Mol.
  Biol. 215 (1990)
• Altschul, Madden, Schaffer, Zhang, Zhang, Miller,
  Lipman, Nucleic Acids Res. 25 (1997)
• Main ideas
• Increase search speed by finding fewer, but
  better, hot spots during initial screening phase
• Uses longer word sizes
• Integrate scoring matrix into first phase
• Compare with FASTA, which requires exact matches

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BLAST
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Hits

• For each word, evaluate score of match (exact or
  not) according to BLOSUM62 substitution matrix
• e.g., for PQG exact match with PQG
• score is 756 18
• There are 20w possible w-length words, but
  considering only those with score gt t, greatly
  reduces number of matches
• e.g., there are 203 8000 possible matches to
  PQG,
• but only 50 achieve score gt t 13

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BLAST Hits

• A hit is a w-length word in the database that
  aligns with a word from the query sequence with
  score gt t
• BLAST looks for hits instead of exact matches
• Allows word size to be kept high for speed,
  without sacrificing sensitivity
• Typically, w 3-5 for amino acids,
• w 11-12 for DNA
• t is the most critical parameter
• ?t ?? ? background hits (faster)
• ?t ?? ? ability to detect more distant
  relationships (at cost of increased noise

33
Extending a hit

• After locating a hit, BLAST attempts to extend
  hit in both directions, until score has drops
  more than X below the maximum score yet attained.
  
• Extension step typically accounts for gt 90 of
  execution time.

34
Extending a hit
35
Improvement 2-hit method

• Do extensions only when there are two hits on the
  same diagonal within some distance A of each
  other (e.g., A 40)
• Reduces sensitivity (ability to detect distantly
  related sequences)
• To compensate, use lower t value (e.g., 11 rather
  than 13)
• Because we only extend when there are two nearby
  hits, many fewer regions are extended

36
BLAST Terminology

• Segment pair equal-length substrings of
  sequences S1 and S2
• Locally maximal segment pair segment pair whose
  alignment score cannot be improved by extending
  or shortening it
• Maximum segment pair (MSP) segment pair with
  maximum score over all segment pairs in the
  sequences S1 and S2
• High-scoring segment pair (HSP) A segment pair
  with score higher than some cutoff score, s.
• w is the length parameter t is the threshold
  parameter

37
Gapped BLAST

• Allows local alignments with indels (similar to
  FASTA)
• Local alignments from different diagonal are
  merged into a different local alignment followed
  by some indels followed by a second local
  alignment, etc.
• equivalent to a path through the dynamic
  programming matrix composed of alternating
  diagonal sections and paths connecting them

38
Gapped BLAST

• Original BLAST implicitly handled gaps by finding
  several distinct HSPs and calculating a
  statistical assessment of the combined result
• Two or more HSPs each below the cutoff value
  might in combination rise to statistical
  significance
• Gapped BLAST, extend hits by allowing gaps when
  hits are promising (exceed sg)
• Advantage We can afford to miss some HSPs as
  long as at least one is found
• Use dynamic programming, starting from center of
  each high-scoring region if s gt sg
• sg is chosen such that gapped alignment is
  triggered in about 1/50 of the sequences compared

39
PSI-BLAST

• Position-Specific Iterated BLAST
• Generates a multiple alignment from statistically
  significant alignments produced by BLAST
• Produces a Position-Specific Scoring Matrix
  (PSSM)
• Can search the database using the PSSM
• Match sequences to profile
• Generate new profiles
• Repeat (iteration)
• Search gradually extends to increasingly
  divergent sequences

40
Flavors of BLAST

• BLASTP - protein query against protein DB
• BLASTN - DNA/RNA query against DNA (GenBank)
• BLASTX - 6-frame translated DNA query against
  proteinDB
• TBLASTN - protein query against 6-frame DNA
  translation
• TBLASTX - 6-frame DNA query to 6-frame DNA
  translation
• PSI-BLAST - protein "profile" query against
  protein DB
• PHI-BLAST - protein pattern against protein DB

41
Questions?

• What are substitution matrices?
• 2 Major types PAM BLOSUM
• PAM Point Accepted Mutation - relies on
  "evolutionary model" based on observed
  differences in closely related proteins
• Model includes defined rate for each type of
  sequence change
• Suffix number (n) reflects amount of "time"
  passed rate of expected mutation if n of amino
  acids had changed
• PAM1 - for less divergent sequences (shorter
  time)
• PAM250 - for more divergent sequences (longer
  time)
• BLOSUM BLOck SUbstitution Matrix - based on
  aa substitutions observed in evolutionarily
  divergent proteins
• Doesn't rely on a specific evolutionary model
• Suffix number (n) reflects expected similarity
  average aa identity in the MSA from which the
  matrix was generated
• BLOSUM45 - for more divergent sequences
• BLOSUM60 - for less divergent sequences

42
Questions?

• What does 6-frame translation mean?