1. Multiple Sequence
Comparison

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Outline
Motivation
Multiple Sequence Alignment using Dynamic programming
Multiple Sequence Alignment using Heuristics
Star Alignments
Tree Alignments (CLUSTAL W)
PSI-BLAST and multiple sequence alignment
Evaluation of Alignment Methods
Summary
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3. Pair-wise sequence comparison
“In biomolecular sequences (DNA, RNA, Protein), high sequence similarity usually implies significant functional or structural similarity.”
Underlies the effectiveness of pair-wise sequence
comparison and of biological database searching
Find sequences that have common sub-patterns but
may not have been known to be biologically related.
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4. Multiple sequence comparison
“Evolutionarily and functionally related molecular sequences can differ significantly at the sequence level and yet preserve similar function and/or structure.
Underlies the effectiveness of multiple sequence
comparison.
Deduce unknown conserved patterns from a set of sequences already known to be biologically related.
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5. Common MSA Applications
Characterization and representation of protein families and later identification of other potential members of the family;
Identification and representation of conserved sequence features that correlate with structure and function;
Deduction of evolutionary history.
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6. Common MSA Applications
To detect/demonstrate homology between new sequences and existing families of sequences
To help predict the secondary and tertiary structures of new sequences
To suggest oligonucleotide primers for PCR
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7. Comparing Multiple Sequences
We can compare multiple sequences by aligning the sequences and assigning a score to the alignments
Multiple Sequence Alignment (MSA)
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Application of MSA
Homology
Search
(e.g. BLAST)
Database
top scoring hits
MSA
Conserved regions
Evolution paths
…...
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Definition of MSA
A Multiple Sequence Alignment is obtained by inserting into each sequence a (possibly zero) number of gaps so that the resulting sequences are of the same length and each column has at least one character different from ‘-’ (gap).
IMAGINABLE
IMPRACTICABLE
INFALLIBLE
IM—-AG-INABLE
IMPRACTICABLE
IN-FALLI--BLE
IM-—-AG-INABLE
IM-PRACTICABLE
IN--FALLI--BLE
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10. How to score an alignment?
The Sum-of-Pairs (SP) score:
A multiple alignment implies a pair-wise alignment for each pair of sequences;
SP defines the score of multiple alignment as the sum of scores of all implied pair-wise alignments.
A A C G T A C G A T A
A – C G T A – A A T G
G T C G T A - - T T A
match = 1
mismatch = 0
gap-character = -1
gap-gap = 0 5 3 4
SP score = 12
1 – –2 – = 12
Note: score (-,-) = 0
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11. MSA using dynamic programming
If k sequences of size n then:
O(nk) space and
O(k22knk) time G C -
C G
- G
- -
C G T
- G T
- - -
C G T -
- G T A
- - - A
C G T - G
- G T A -
- - - A G A A T G
nk cells
2k–1 calculations/cell
k(k-1)/2 calculations to compute the SP-score
C G T G
7 calculations/cell
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12. Recall the pair-wise case ?
Question: from 1 to 9 how many paths?
G A A G 1 3 5 2 8 6 9 7 4
Queston: when DP comparison ends – how many possible distinct paths have been explored in total for this example?
Answer: Let us count
Total = 13
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13. Align Multiple Sequences
Queston: when DP comparison ends – how many possible distinct paths have been explored in total for this example?
Answer: Let us count
Total = 13
G A A G
Question: from 1 to 9 how many paths? 1 3 5 2 8 6 9 7 4
Align Multiple
Sequences
Assume we have 3 sequences: AG AC GC
How to do DP? A G C
Question: When DP comparison ends - how many possible distinct path have been explored in total?
Answer: Count!
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14. MSA Using DP with Heuristics
How to cut down the search space (# of calculations) at each step?
One way is to eliminate pairwise projections which does not contribute to the optimal alignment – develop such a test.
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15. Other MSA Methods Using Heuristics
Star Alignment:
Build a multiple alignment based upon the pair-wise alignments between a fixed sequence – called the “center” of the input set and all others.
Tree Alignment:
Build a multiple alignment based upon the pair-wise alignments along edges of a tree relating all the sequences.
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Star Alignment
Given k sequences
Pick one of the sequences as the center
Find optimal pair-wise alignments between the center sequence and each other sequence.
Aggregate the pair-wise alignments (progressive alignment)
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Aggregate Step
Using the center Sc as a guide
Starting with one pairwise alignment, say Sc and S1, and aggregate the rest pairs one at a time
When add one pair (Si, Sc) in, make sure we progressively increase the gaps in Sc to suit further alignment, never removing gaps.
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18. /course/eleg667-01-f/Topic-2c
Star Alignment (cont.)
How should we select the center sequence?
Build a table with the pair-wise similarity score for each pair of sequences.
Choose the sequence with the highest sum of scores.
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Star Alignment (cont.) S2 7
A T T G C C A T T A T G C 1 -1 -3 -5 -7 -9
-11
-13
-15 -2 -4 -6 -8
-10
-12
-14
-16
-18 2 3 4 5 6 S1
S1 = ATTGCCATT
S2 = ATGGCCATT
S3 = ATCCAATTTT
S4 = ATCTTCTT
S5 = ACTGACC
S1 S2 S3 S4 S5 S1 S2 S3 S4 S5 7
-2 –
For k sequences, each size n
Time =T1 = O((k.(k-1)/2).n2) =O(k2.n2)
Score = 7
S1 = ATTGCCATT
S2 = ATGGCCATT
So S1 is picked as the center
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20. /course/eleg667-01-f/Topic-2c
Star Alignment (cont.)
S1 = ATTGCCATT
S5 = ACTGACC--
S2 = ATGGCCATT
S1 = ATTGCCATT--
S3 = ATC-CAATTTT
S4 = ATCTTC-TT S1 S2 S3 S4 S5
S1 = ATTGCCATT
S2 = ATGGCCATT
S3 = ATC-CAATTTT
S4 = ATCTTC-TT
S5 = ACTGACC--
S1 = ATTGCCATT—-
S2 = ATGGCCATT--
S3 = ATC-CAATTTT
S4 = ATCTTC-TT--
S5 = ACTGACC----
For k sequences, each size n, and an upper bound on the alignment length of a:
Time =T2 = O((k-1).n2 + (k-1)2.a )
T1+T2 = O(k2.n2 + k.n2 + (k-1)2.a)
“Once a gap, always a gap”
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21. Issues in Star Alignment
How to select the best anchor ?
How to determine the order of progression ?
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Tree Alignment
Uses a clustering technique to order groups of related sequences in a hierarchical tree;
Based on the tree hierarchy (order from leaves to root), the multiple sequence alignment is generated by aligning and combining groups of sequences;
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23. The Basic Idea of Tree Alignment
(a) A set of sequences S2 S5 S4 S2 S5 S7 S3 S6 S8 S1 S9
(a) A set of sequences
(c) A pair-wise distance matrix
S1 S2 S3 S4 S5 S6 S7 S8 S9 S1 S2 S3 S4 S5 S6 S7 S8 S9
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Question: In general, given pair-wise distances between a set S of objects (e.g. distance matrix), how to derive a weighted tree T where each leaf of T corresponds to an object in S, and the distance between two leafs i, j correspond to the distance between i and j in S?
Answer: This problem is an important problem in computation biology, and has been studied by many authors using a variable of techniques.
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25. Clustal W – A Tool of Progressive Multiple Sequence Alignment with Improved Sensitivity

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CLUSTAL W (Cont.)
All pairs of sequences are aligned separately in order to calculate a distance matrix giving the divergence of each pair of sequences;
A guide tree is calculated from the distance matrix;
The sequences are progressively aligned according to the branching order in the guide tree.
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27. /course/eleg667-01-f/Topic-2c
CLUSTAL W (Cont.) S1 S3 S2 S4
Guide Tree S1 S2 S3 S4
S1 S2 S3 S4 S1 S2 S3 S4
D12D13 D14
D23 D24
D34
Distance matrix
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CLUSTAL W (Cont.) S2
Align most
similar pair S4
Guide Tree
gaps to optimize alignment S1
Align next most
similar pair S3 S2 S1 S3 S2 S4 S4
Align alignments,
preserve gaps S1 S3
new gap to optimize alignment
of (S1S3)with (S2S4 )
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29. Clastal-W: Some Implementation Hints
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30. /course/eleg667-01-f/Topic-2c
Distance Matrix
Initially all sequences are pairwised aligned.
S1 S2 S3 S4 S5 S6 S7 S2 S3 S4 S5 S6 S7 S8 S1 S2 Sn S3 S1 S2
= 7 7 S3 S1
= 8 8 5
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31. Two Options for Pairwise Alignment
Fast approximate method (Bashford,D.,Chothia.,C., 1987,J.Mol.Biol.)
Allows large number of seqs to be aligned even on a microcomputer
Fully dynamic programming alignments (Myers,E.,Miller,W., 1988,CABIOS)
Two gap penalties
Full weight matrix
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32. /course/eleg667-01-f/Topic-2c
The Guide Tree
Unrooted tree
Calculated from distance matrix
( Neighbour-Joining Method )
Rooted tree
Calculated from unrooted tree
( Middle Point Method )
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33. /course/eleg667-01-f/Topic-2c
Unrooted Tree
Neighbor Joining Method provides not only the topology but also the branch lengths (Fitch, Margoliash) of the final tree
Each node represents a sequence
Each path length represents the distance between two specific sequences
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34. Unrooted Tree - ExampleS1 S4 L1 S5 L4 L5 A E B L2 L8 C D S2 L3 S8 L6 L7 F S3 S7 S6
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35. Neighbour Joining MethodS8 S7 S1 S7 S1 S8 S6 S2 S6 X X Y S2 S3 S5 S3 S5 S4 S4
S12 = Sum of all branch lengths = f(D’s)
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36. /course/eleg667-01-f/Topic-2c
NJ-Method Example
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PSI-BLAST
Observation
Database searches using position-specific score matrices, also called profiles or motifs, often are much better able to detect weak relationships than are database searches that use a simple sequences as query
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PSI-BLAST
Cont’d
PSI-BLAST uses a procedure to contruct a position-specific score matrix automatically from the output of a BLAST run, and modified BLAST to operate using such a matrix in the place of a simple query
The resulting PSI-BLAST program often is substantially more sensitive than the corresponding BLAST program.
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39. PSI-BLAST and Multiple Sequence Alignment
PSI-BLAST also produce a multiple sequence alignment with the query sequence as a master template
Collect all hits with E-value below a theshold-say 0.01, and
Do not include copies of sequences identical to the query
Retain one copy for each hit which is very similar to the query
Other details
The MSA constructed is used by PSI-BLAST for construction a scoring matrix
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40. Where PSI-BLAST Differ from Other “True” MSA Methods?
PSI-BLAST deals with local alignments, so each columns of M (the multiple alignment) may involve varying numbers of sequences. In fact, some columns may include only the query sequence itself.
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41. Classification of Multiple Sequence Alignment Methods
MSA
Progressive
Iterative
(local)
Global Alignment
Local Alignment
DALIGN
PIMA
HMM
(HMMT)
STAR
Tree
Genetic
Algorithm
(SAGA)
MULTAL
MULTALIGN
PILEUP
CIUSTA-W
PSI-BLAST
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42. How to Compare Alignment Software ?

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CASA -- A Server for the Critical Asessment of
Protein Sequence Alignment Accuracy
Sequence alignment
Structural alignment
Fasta proteins
database
User 1
H i g h S p e e d N e t w o r k
Benchmark Server
CE alignments
Web Interactive
Benchmarking
Program
User 4
User 3
User 2
ASVIE-AAVI
VIVI-EPAAG
A-SVIE-AAV-
VIVI-EPAAG
Remote users
Download fasta sequences
Produce set of sequence alignment
Submit the resulted alignments
Benchmarking program evaluates parameters
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44. /course/eleg667-01-f/Topic-2c
Methods of Discovery of Biological Sequence Homology
Alignment
Pattern Matching
Pair wise
MSA
Eventuation
All and
verify
Scan Seeds
And
???
Combined
Optimal
Heuristic
Heuristic
FLASH
Global
Local
FAST
BLAST
Progressive
Iterative
MOTIF/
ASSET
(See slide 41)
Discover DP
PRATT
TEIRESIAS
ATGC
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