Multiple Sequence Alignment

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Multiple Sequence Alignment

• Urmila Kulkarni-Kale
• Bioinformatics Centre
• University of Pune
• urmila_at_bioinfo.ernet.in

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Approaches MSA

• Dynamic programming
• Progressive alignment ClustalW
• Genetic algorithms SAGA

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Progressive alignment approach

• Align most related sequences
• Add on less related sequences to initial
  alignment
• Perform pairwise alignments of all sequences
• Use alignment scores to produce phylogenetic tree
• Align sequences sequentially, guided by the tree
• Gaps are added to an existing profile in
  progressive methods

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No of pairwise alignments N(N-1)/2
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(No Transcript)
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Pairwise alignment Calculate the distance matrix
Unrooted Neighbor-joining tree
Rooted NJ tree Sequence weights
Progressive alignment using Guide tree
Steps in ClustalW Algorithm
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ClustalW weight

• groups of related sequences receive lower weight
• highly divergent sequences without any close
  relatives receive high weights

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ClustalW affine Gap penalty

• GOP Gap Opening Penalty
• GEP Gap Extension Penalty
• Heuristics in calculating gap penalty
• Position specific penalty
• gap at position?
• yes ? lower GOP and GEP
• no, but gap within 8 residues ? increase GOP
• stretch of hydrophilic residues?
• yes ? lower GOP
• no ? use residue-specific gap propensities

Once a gap, always a gap
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Variation in local GOP
Lowest GOP in Hydrophilic regions
Initial GOP
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MSA help detect Similarity
Hemoglobin Human, chimpanzee, Goat, pig, horse
mouse
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Sample MSA
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Applications of MSA

• Detecting diagnostic patterns
• Phylogenetic analysis
• Primer design
• Prediction of protein secondary structure
• Finding novel relationships between genes
• Similar genes conserved across organisms
• Same or similar function
• Simultaneous alignment of similar genes yields
• regions subject to mutation
• regions of conservation
• mutations or rearrangements causing change in
  conformation or function

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Limitations of Progressive alignment approach

• Greedy nature
• Any errors in the initial alignment are carried
  through
• More efficient for closely related sequences than
  for divergent sequences