1. GA for Sequence Alignment  Pair-wise alignment  Multiple string alignment

2. Pairwise Sequence Alignment  VNRLQQNIVSLEVDHKVANYKP  VNRLQQSIVSLRDAFNDGELD HRVLNYKP  Solving by a dynamic programming using Dayhoff matrics  Each pairwise alignment needs O(n 1 n 2 )  VNRLQQNIVSL__________EVDHKVANYKP  VNRLQQSIVSLRDAFND GELD HRVLNYKP

3. How to implement a GA ?  Representation  Fitness  Operators design  Selection strategy

4. Pair-wise Alignment: Representation  How do you think?  For example (my intuitively way) –Guess a length n –Chromosome

5. Pair-wise Alignment: Representation  So the chromosome becomes:  You can also use the gap position (1,2,4,5,6,8….) (2,4,5,7,8,10….)

6. Pair-wise Alignment: Fitness Function  Simplest –Match : 1 –Dismatch : -2 –Gap : -1  Using the scoring matrix –Protein : PAM,… –DNA: substitution matrix  Summarize the total score.

7. Pair-wise Alignment: Genetic Operators  All our previous operators. –Image one!!!  Selection –Try it!!!

8. Conclusion About Pair-wise Alignment  DP can solve it in O(NM)  GA can’t have too much advantage.

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10. Multiple String Alignment: Representation  How do you think?  For example (my intuitively way) –Guess a length n –Chromosome

11. Multiple String Alignment: Representation  So the chromosome becomes:  You can also use the gap position –Need fewer space –Some good operators….. (1,2,4,5,6,8….) (2,4,5,7,8,10….) …

12. Multiple String Alignment: Fitness Function  The most hard part  You can never know what is the real scoring system! Even biologists!!!  Approximation –Using SOP (sum of pairs) The most widely used Using PAM,… –Motif-based…