Introduction to Bioinformatics From Pairwise to Multiple Alignment.
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Transcript of 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