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Multiple Sequence Alignment

Multiple Sequence Alignment. Urmila Kulkarni-Kale Bioinformatics Centre University of Pune urmila@bioinfo.ernet.in. Approaches: MSA. Dynamic programming Progressive alignment: ClustalW Genetic algorithms: SAGA. Progressive alignment approach. Align most related sequences

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Multiple Sequence Alignment

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  1. Multiple Sequence Alignment Urmila Kulkarni-Kale Bioinformatics Centre University of Pune urmila@bioinfo.ernet.in

  2. Approaches: MSA • Dynamic programming • Progressive alignment: ClustalW • Genetic algorithms: SAGA

  3. 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

  4. No of pairwise alignments: N*(N-1)/2

  5. Pairwise alignment: Calculate the distance matrix Unrooted Neighbor-joining tree Rooted NJ tree Sequence weights Progressive alignment using Guide tree Steps in ClustalW Algorithm

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

  7. 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

  8. Highest GOP in ‘Gapped regions’ Variation in local GOP Lowest GOP in Hydrophilic regions Initial GOP

  9. MSA: help detect Similarity Hemoglobin: Human, chimpanzee, Goat, pig, horse & mouse

  10. Sample MSA

  11. 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

  12. 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

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