1. Multiple Sequence Alignment
Urmila Kulkarni-Kale
Bioinformatics Centre
University of Pune
October 2k5

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

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
October 2k5

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

5. October 2k5

6. 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

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

8. 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

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

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

11. Sample MSA
October 2k5

12. 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

13. 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