1. Multiple Sequence Alignment Dr. Urmila Kulkarni-Kale Bioinformatics Centre University of Pune urmila@bioinfo.ernet.in Urmila.kulkarni.kale@gmail.com

2. Jan 19, 2010© UKK, Bioinformatics Centre, UoP2 Approaches: MSA Dynamic programming Progressive alignment: ClustalW Genetic algorithms: SAGA

3. Jan 19, 2010© UKK, Bioinformatics Centre, UoP3 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. Jan 19, 2010© UKK, Bioinformatics Centre, UoP4 No of pairwise alignments: N*(N-1)/2

5. Jan 19, 2010© UKK, Bioinformatics Centre, UoP5

6. Jan 19, 2010© UKK, Bioinformatics Centre, UoP6 Pairwise alignment: Calculate the distance matrix Unrooted Neighbor-joining tree Rooted NJ tree Sequence weights Progressive alignment usingGuide tree Steps in Clustal W Algorithm

7. Jan 19, 2010© UKK, Bioinformatics Centre, UoP7 Clustal W: weight groups of related sequences receive lower weight highly divergent sequences without any close relatives receive high weights

8. Jan 19, 2010© UKK, Bioinformatics Centre, UoP8 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

9. Jan 19, 2010© UKK, Bioinformatics Centre, UoP9 Highest GOP in ‘Gapped regions’ Variation in local GOP Initial GOP Lowest GOP in Hydrophilic regions

10. Jan 19, 2010© UKK, Bioinformatics Centre, UoP10 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

11. Jan 19, 2010© UKK, Bioinformatics Centre, UoP11 Sample MSA

12. Jan 19, 2010© UKK, Bioinformatics Centre, UoP12 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