Multiple Sequence Alignment

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Presentation transcript:

Multiple Sequence Alignment Urmila Kulkarni-Kale Bioinformatics Centre University of Pune urmila@bioinfo.ernet.in October 2k5

Approaches: MSA Dynamic programming Progressive alignment: ClustalW Genetic algorithms: SAGA October 2k5

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 October 2k5

No of pairwise alignments: N*(N-1)/2 October 2k5

October 2k5

Steps in ClustalW Algorithm Pairwise alignment: Calculate the distance matrix Unrooted Neighbor-joining tree Rooted NJ tree Sequence weights Progressive alignment using Guide tree Steps in ClustalW Algorithm October 2k5

ClustalW: weight groups of related sequences receive lower weight highly divergent sequences without any close relatives receive high weights October 2k5

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 October 2k5

Variation in local GOP Lowest GOP in Hydrophilic regions Highest GOP in ‘Gapped regions’ Initial GOP October 2k5

MSA: help detect Similarity Hemoglobin: Human, chimpanzee, Goat, pig, horse & mouse October 2k5

Sample MSA October 2k5

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 October 2k5

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 October 2k5