Download Multiple Sequence Alignment

Multiple Sequence Alignment
Dr. Urmila Kulkarni-Kale
Bioinformatics Centre
University of Pune
[email protected]
[email protected]
Approaches: MSA
• Dynamic programming
• Progressive alignment: ClustalW
• Genetic algorithms: SAGA
Jan 19, 2010
© UKK, Bioinformatics Centre, UoP
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Progressive alignment approach
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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
Jan 19, 2010
© UKK, Bioinformatics Centre, UoP
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No of pairwise alignments: N*(N-1)/2
Jan 19, 2010
© UKK, Bioinformatics Centre, UoP
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Jan 19, 2010
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Pairwise alignment:
Calculate the distance matrix
Unrooted Neighbor-joining tree
Rooted NJ tree
Sequence weights
Progressive alignment using
Guide tree
Jan 19, 2010
Steps in Clustal W Algorithm
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© UKK, Bioinformatics Centre, UoP
Clustal W: weight
• groups of related sequences receive lower
weight
• highly divergent sequences without any
close relatives receive high weights
Jan 19, 2010
© UKK, Bioinformatics Centre, UoP
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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
Jan 19, 2010
© UKK, Bioinformatics Centre, UoP
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Variation in local GOP
Lowest GOP in
Hydrophilic
regions
Highest GOP in ‘Gapped regions’
Initial GOP
Jan 19, 2010
© UKK, Bioinformatics Centre, UoP
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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
Jan 19, 2010
© UKK, Bioinformatics Centre, UoP
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Sample MSA
Jan 19, 2010
© UKK, Bioinformatics Centre, UoP
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Applications of MSA
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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
Jan 19, 2010
© UKK, Bioinformatics Centre, UoP
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