introduction to bioinformatics from pairwise to multiple alignment

Introduction to Bioinformatics

From Pairwise to Multiple Alignment

Outline

• Advances in BLAST

• Multiple Sequence Alignment- CLUSTAL

Scoring system for BLAST

Substitution Matrix +

Gap Penalty

Substitution Matrix

• BLOSUM matrices are based on the replacement patterns found in more highly conserved regions of the sequences without gaps

• PAM matrices based on mutations observed throughout a global alignment, includes both highly conserved and highly mutable regions

Gap penalty

• Example showed -1 score per indel– So gap cost is proportional to its length

• Biologically, indels occur in groups– We want our gap score to reflect this

• Standard solution: affine gap model– Once-off cost for opening a gap– Lower cost for extending the gap– Changes required to algorithm

Statistical significance

E-value• The number of hits (with the same similarity score) one can

"expect" to see just by chance when searching the given string in a database of a particular size.

• higher e-value lower similarity– “sequences with E-value of less than 0.01 are almost always

found to be homologous”

• The lower bound is normally 0 (we want to find the best)

Expectation Values

Increases linearly with

length of query sequence

Increases linearly with

length of database

Decreases exponentially with score of

alignment

Remote homologues

• Sometimes BLAST isn’t enough.

• Large protein family, and BLAST only gives close members. We want more distant members

PSI-BLAST

PSI-BLAST

• Position Specific Iterated BLAST

Regular blast

Construct profile from blast results

Blast profile search

Final results

PSI-BLAST

• Advantage: PSI-BLAST looks for seqs that are close to ours, and learns from them to extend the circle of friends

• Disadvantage: if we found a WRONG sequence, we will get to unrelated sequences. This gets worse and worse each iteration

Multiple Sequence Alignment

MSA

VTISCTGSSSNIGAG-NHVKWYQQLPGVTISCTGTSSNIGS--ITVNWYQQLPGLRLSCSSSGFIFSS--YAMYWVRQAPGLSLTCTVSGTSFDD--YYSTWVRQPPGPEVTCVVVDVSHEDPQVKFNWYVDG--ATLVCLISDFYPGA--VTVAWKADS--AALGCLVKDYFPEP--VTVSWNSG---VSLTCLVKGFYPSD--IAVEWWSNG--

Like pairwise alignment BUT compare n sequences instead of 2

Rows represent individual sequences Columns represent ‘same’ position

May be gaps in some sequences

Why multiple alignments?

BLAST Usually obtains many sequences that are significantly similar to the query sequence

PracticallyComparing each and every sequence to every other may impractical when the number of sequences is large

Solution generating a profile

MSA MSA can give you a better picture of functional sites on proteins

and nucleic acids as well as the forces that shape evolution!

VTISCTGSSSNIGAG-NHVKWYQQLPGVTISCTGSSSNIGS--ITVNWYQQLPGLRLSCTGSGFIFSS--YAMYWYQQAPGLSLTCTGSGTSFDD-QYYSTWYQQPPG

• Important amino acids or nucleotides are not allowed to mutate• Less important positions change more easily

Alignment Example

GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC

GTCGTAGTCGGCTCGACGTCTAGCGAGCGTGATGCGAAGAGGCGAGCGCCGTCGCGTCGTAAC

1*12*0.7511*0.5

Score=8

4*111*0.752*0.5

Score=13.25

Score : 4/4 =1 , 3/4 =0.75 , 2/4=0.5 , 1/4= 0

Example of 3 sequences:

Dynamic Programming

• Pairwise A–B alignment table– Cell (i,j) = score of best alignment between first i

elements of A and first j elements of B– Complexity: length of A length of B

• 3-way A–B–C alignment table– Cell (i,j,k) = score of best alignment between first i

elements of A, first j of B, first k of C– Complexity: length A length B length C

• Example: protein family alignment– 100 proteins, 1000 amino acids each– Complexity: 10300 table cells– Calculation time: beyond the big bang!

Feasible Approach

• Based on pairwise alignment scores– Build n by n table of pairwise scores

• Align similar sequences first– After alignment, consider as single sequence– Continue aligning with further sequences

– For n sequences, there are n(n-1)/2 pairs

GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC

1 GTCGTAGTCG-GC-TCGAC2 GTC-TAG-CGAGCGT-GAT3 GC-GAAGAGGCG-AGC4 GCCGTCGCGTCGTAAC

1 GTCGTA-GTCG-GC-TCGAC2 GTC-TA-G-CGAGCGT-GAT3 G-C-GAAGA-G-GCG-AG-C4 G-CCGTCGC-G-TCGTAA-C

CLUSTAL method

• Higgins and Sharp 1988 – ref: CLUSTAL: a package for performing

multiple sequence alignment on a microcomputer. Gene, 73, 237–244. [Medline]

An approximation strategy (heuristic algorithm) yields a possible alignment, but not necessarily the best one

Progressive Sequence Alignment

ABCD

A B C D

A

B 11

C 3 1

D 2 2 10

Compute the pairwise Compute the pairwise alignments for all against allalignments for all against all

the similarities are stored in a the similarities are stored in a tabletable

First step:

A B C D

A

B 11

C 3 1

D 2 2 10

A

D

C

B

cluster the sequences to create a cluster the sequences to create a treetree

•Represents the order in which pairs of Represents the order in which pairs of sequences are to be alignedsequences are to be aligned•similar sequences are neighbors in the similar sequences are neighbors in the tree tree •distant sequences are distant from distant sequences are distant from each other in the treeeach other in the tree

Second step:

N Y L S N K Y L S N F S N F L S

N K/- Y L S N F L/- S

N K/- Y/F L/- S

Join alignments

Treating Gaps in ClustalW

• Penalty for opening gaps and additional penalty for extending the gap

• Gaps found in initial alignment remain fixed

• New gaps are introduced as more sequences are added (decreased penalty if gap exists)

• Decreased within stretches of hydrophilic residues

MSA Approaches• Progressive approach

CLUSTALW (CLUSTALX)http://www.ebi.ac.uk/clustalw/

PILEUPT-COFFEE

• Iterative approach: Repeatedly realign subsets of sequences.

MultAlin, DiAlign.

• Statistical Methods:Hidden Markov Models

SAM2K

• Genetic algorithmSAGA