Download What is a Multiple Sequence Alignment?

COT 6930
HPC and Bioinformatics
Multiple Sequence Alignment
Xingquan Zhu
Dept. of Computer Science and Engineering
Outline

Multiple Sequence Alignment


What, Why, and How
Multiple Sequence Alignment Methods



Multidimensional dynamic programming
Star Alignment
Tree Alignment


Progressive Alignment
 Clustalw: a widely used algorithm
Iterative Alignment

Genetic Algorithm
What is a Multiple Sequence Alignment?



Pairwise alignments: involve two sequences
Multiple sequence alignments: involve more than 2
sequences (often 100’s, either nucleotide or protein).
A formal definition


Hs
Sp
Tg
Pf
A multiple alignment of strings S1, … Sk is a series of strings
with spaces such that |S1’| = … = |Sk’|
Sj’ is an extension of Sj by insertion of spaces
Goal: Find an optimal multiple alignment.
---MK-------MKKFQLF
MTAAKKLSLF
--------MN
--LSLVAAML
SILSYFVALF
SLAALFCLLS
QIRPYILLLI
LLLSAARAEE
LLPMAFASGD
VATLRPVAAS
VSLLKFISAV
EDKK-EDVGT
DNST-ESYGT
DAEEGKVKDV
DSN---IEGP
VVGIDLGTTY
VIGIDLGTTY
VIGIDLGTTY
VIGIDLGTTY
Why we do multiple alignments?


In order to reveal the relationship between a group of
sequences (homology)
Simultaneous alignment of similar gene sequences
may




Discover the conserved regions in genes
Determine the consensus sequence of these aligned
sequences
Help defines a protein family that may share a common
biochemical function or evolutionary origin and thus
reveals an evolutionary history of the sequences.
Help prediction of the secondary and tertiary structures
of new sequences
MSA Methods

Multidimensional dynamic programming


Star Alignment, Tree Alignment, Progressive
Alignment


Extension of DP to multiple (3) sequences
Starting with an alignment of the most alike
sequences and building an alignment by adding
more sequences
Iterative methods

Making an initial alignment of groups of sequences
and revising the alignment to achieve a more
reasonable result
Outline

Multiple Sequence Alignment


What, Why, and How
Multiple Sequence Alignment Methods



Multidimensional dynamic programming
Star Alignment
Tree Alignment


Progressive Alignment
 Clustalw: a widely used algorithm
Iterative Alignment

Genetic Algorithm
Multiple Sequence Alignment by DP

Pairwise sequence alignment


Extension to 3 sequences


a scoring matrix where each position provides the
best alignment up to that point
the lattice of a cube that is to be filled with
calculated dynamic programming scores.
Scoring positions

on 3 surfaces of the cube represent the alignment
of a pair
Scoring of MSA: Sum of Pairs

Scores = summation of all possible
combinations of amino acid pairs

-
I
K
S
I
K
S
S
E

Using BLOSUM62 matrix,
gap penalty -8
In column 1, we have
pairs


-8 - 8 + 4 = -12


-,S
-,S
S,S
k(k-1)/2 pairs per column
Sum of Pairs

Given 5 sequences:
NCCE
NNCE
N - CN
SC SN
SC SE

How many possible combinations of pairwise alignments
for each position?
5!
C 
 10
2!3!
5
2
Sum of Pairs
Assume: match/mismatch/gap = 1/0/-1
N C C E
N N C E
N - C N
S C S N
S C S E
The 1st position: # of N-N (3), # of S-S (1), # of N-S (6)
SP(1) = 4*1 + 0*6 + (-1)*0 = 4
The 2nd position: # of C-C (3), # of N-C (3), # of gaps (4),
SP(2) = 3*1 + 0*3 + (-1)*4 = -1

Dynamic programming matrix
Pairwise alignment
Seq 2
G T G C T T G A
T
Seq 1 G
Match/Mismatch
G
C
C
T
Gap in sequence 2
Gap in
sequence 1
Dynamic programming matrix
Multiple sequence alignment
Seq 1
S
M
M
A
Seq 3
V
Seq 2
S
M
T
V
many possibilities
DP Alignment Examples

All three match/mismatch
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Sequence 1 & 2 match/mismatch with gap in 3

Sequence 1 & 3 match/mismatch with gap in 2

Sequence 2 & 3 match/mismatch with gap in 1

Sequence 1 with gaps in 2 & 3
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Sequence 2 with gaps in 1 & 3

Sequence 3 with gaps in 1 & 2

Choose the largest value among the above
seven possibilities
Computational Complexity

For protein sequences each 300 amino acid in length
& excluding gaps, with DP algorithm
Two sequences, 3002 comparisons
 Three sequences, 3003 comparisons
 N sequences, 300N comparisons
O(LN) L: length of the sequences; N: number of sequences


The number of comparisons & memory required are
too large for n > 3 and not practical
Outline

Multiple Sequence Alignment


What, Why, and How
Multiple Sequence Alignment Methods



Multidimensional dynamic programming
Star Alignment
Tree Alignment


Progressive Alignment
 Clustalw: a widely used algorithm
Iterative Alignment

Genetic Algorithm
Star Alignments




Heuristic method for multiple sequence alignments
Select a sequence sc as the center of the star
For each sequence s1, …, sk such that index i  c,
perform a global alignment (using DP)
Aggregate alignments with the principle “once a gap,
always a gap.”
Star Alignments Example
s1
MPE
MSKE
| |
- ||
MKE
MKE
s1:
s2:
s3:
s4:
s3
s2
MPE
MKE
MSKE
SKE
MKE
||
SKE
s4
MPE
MKE
-MPE
-MKE
MSKE
-MPE
-MKE
MSKE
-SKE
Choosing a center


Try them all and pick the one with the best
score
Calculate all O(k2) alignments, and pick the
sequence sc that maximizes
 score(s , s )
i c
i
c
Star Alignment Example
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
S1=ATTGCCATT
S2=ATGGCCATT
S3=ATCCAATTTT
S4=ATCTTCTT
S5=ATTGCCGATT
s1
s1
s2
s3
s4
s5
7
-2
0
-3
2
-2
0
-4
1
0
-7
-11
-3
-3
s2
7
s3
-2
-2
s4
0
0
0
s5
-3
-4
-7
-3
-17
Star Alignments Example
Merging Pairwise Alignment
Star Alignment Example
Merging Pairwise Alignment
Analysis
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Assuming all sequences have length n
O(n2) to calculate global alignment
O(k2) global alignments to calculate
Using a reasonable data structure for joining
alignments, no worse than O(kl), where l is
upper bound on alignment lengths
O(k2n2+kl)=O(k2n2) overall cost
Outline

Multiple Sequence Alignment


What, Why, and How
Multiple Sequence Alignment Methods



Multidimensional dynamic programming
Star Alignment
Tree Alignment


Progressive Alignment
 Clustalw: a widely used algorithm
Iterative Alignment

Genetic Algorithm
Tree Alignment
 Compute the overall similarity based on pairwise
alignment along the edge
Consensus String
sequence
sequence S1
sequence S2
weight : sim(s1,s2)
sequence

The sum of all these weights is the score of the tree
The consensus string derived from multiple alignment is
the concatenation of the consensus characters for each
column. The consensus character for column is the
character that minimizes the summed distance to it from all
the characters in column
Tree Alignment Example

 p(a, b)  1 if a  b

 p(a,b)  0 if a  b
 p(a,-)  -1

Scoring system used is
CAT
- GT
CAT
3 CAT
0
GT
CTG
C-G
CTG
3
1
CTG
1
CG
We have a score of 8
Tree Alignment Example
Example
Example
Example
Example
Example
Example
Example
Analysis


We don’t know the correct tree
Without the tree, the tree alignment
problem is NP-complete

Likely only exponential time solution
available (for optimal answers)
Outline

Multiple Sequence Alignment


What, Why, and How
Multiple Sequence Alignment Methods



Multidimensional dynamic programming
Star Alignment
Tree Alignment


Progressive Alignment
 Clustalw: a widely used algorithm
Iterative Alignment

Genetic Algorithm
Progressive Methods



DP-based MSA program is limited in 3 sequences or
to a small # of relatively short sequences
Progressive alignments uses DP to build a msa
starting with the most related sequences and then
progressively adding less-related sequences or
groups of sequences to the initial alignment
Most commonly used approach
Progressive Methods
Progressive alignment is heuristic.
 It does not separate the process of scoring an
alignment from the optimization algorithm
 It does not directly optimize any global scoring
scoring function of “alignment correctness”.
 It is fast, efficient and the results are reasonable.
We will illustrate this using ClustalW.

Progressive MSA occurs in 3 stages
1.
Do a set of global pairwise alignments
(Needleman and Wunsch)
2.
Create a guide tree
3.
Progressively align the sequences
ClustalW Procedure
Progressive Methods: ClustalW

http://www.ebi.ac.uk/clustalw/

ClustalW is a general purpose multiple
alignment program for DNA or proteins.

ClustalW: The W standing for “weighting” to
represent the ability of the program to provide
weights to the sequence and program
parameters.

CLUSTALX provides a graphic interface
Use Clustal W to do a progressive MSA
Operational options
Output options
Input options, matrix
choice, gap opening
penalty
Gap information,
output tree type
File input in GCG,
FASTA, EMBL, GenBank,
Phylip, or several other
formats
Progressive MSA stage 3 of 3 :
progressive alignment
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Make a MSA based on the order in the guide
tree
Start with the two most closely related
sequences
Then add the next closest sequence
Continue until all sequences are added to the
MSA
Problems w/ Progressive Alignment

Highly sensitive to the choice of initial pair to
align.


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The very first sequences to be aligned are the
most closely related on the sequence tree. If
alignment good, few errors in the initial alignment
The more distantly related these sequences, the
more errors
Errors in alignment propagated to the MSA
Outline

Multiple Sequence Alignment


What, Why, and How
Multiple Sequence Alignment Methods



Multidimensional dynamic programming
Star Alignment
Tree Alignment


Progressive Alignment
 Clustalw: a widely used algorithm
Iterative Alignment

Genetic Algorithm
Iterative Methods





Results do NOT depend on the initial pairwise
alignment (recall progressive methods)
Starting with an initial alignment and repeatedly
realigning groups of the sequences
Repeat until one MSA doesn’t change significantly
from the next.
After iterations, alignments are better and better.
An example is genetic algorithm approach.
Genetic Algorithms





A general problem solving method modeled on
evolutionary change.
Inspired by the biological evolution process
Uses concepts of “Natural Selection” and “Genetic
Inheritance” (Darwin 1859)
Create a set of candidate solutions to your problem,
and cause these solutions to evolve and become
more and more fit over repeated generations.
Use survival of the fittest, mutation, and crossover to
guide evolution.
Genetic Search Algorithms
Random generation
(candidate solutions)
Evaluation (fitness
function)
Selection (candidate
solutions with larger
fitness values will have
larger chance to be
included)
Crossover + Mutation
(change some selected
candidate solutions to
converge to the optimal
solution and to prevent a
local extreme
Outline

Multiple Sequence Alignment


What, Why, and How
Multiple Sequence Alignment Methods



Multidimensional dynamic programming
Star Alignment
Tree Alignment


Progressive Alignment
 Clustalw: a widely used algorithm
Iterative Alignment

Genetic Algorithm