1. Introduction to Bioinformatics - Tutorial no. 2 Global Alignment Local Alignment

2. DP – what does it mean? Principle of reduction of number of paths that need to be examined: If a path from X→Z passes through Y, the best path from X→Y is independent of the best path from Y→Z

3. Global vs. Local alignment Dotplot showing identities between short name ( DOROTHYHODGKIN ) and full name ( DOROTHYCROWFOOT HODGKIN ) of a famous protein crystallographer. S 1 = DOROTHYHODGKIN S 2 = DOROTHYCROWFOOTHODGKIN

4. Global vs. Local alignment Dotplot showing identities between short name ( DOROTHYHODGKIN ) and full name ( DOROTHYCROWFOOT HODGKIN ) of a famous protein crystallographer. Global alignment: DOROTHY--------HODGKIN DOROTHYCROWFOOTHODGKIN

5. Local Alignment The problem: we want to find the substrings of s and t with highest similarity. Scoring System: just as in global alignment:  Match: +1  Mismatch: -1  Indel: -2

6. Local Alignment – cont ’ d The differences: 1. We can start a new match instead of extending a previous alignment.  This means- at each cell, we can start to calculate the score from 0 (even if this means ignoring the prefix).  We do this only if it’s better than the alternative (which means- only if the alternative is negative). 2. Instead of looking only at the far corner, we look anywhere in the table for the best score (even if this means ignoring the suffix)

7. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0 T 1 A 2 A 3 T 4 A 5

8. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 00 T 1 A 2 A 3 T 4 A 5 T-T-

9. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 A 2 A 3 T 4 A 5 TACTAA ------

10. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 0 A 2 0 A 3 0 T 4 0 A 5 0 ----- TAATA

11. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 01 A 2 0 A 3 0 T 4 0 A 5 0 TTTT

12. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 01? A 2 0 A 3 0 T 4 0 A 5 0 TA T- TA- --T -2 TA -T 0

13. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 01001 A 2 0 A 3 0 T 4 0 A 5 0 TACT ---T

14. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 0100100 A 2 0020021 A 3 0 T 4 0 A 5 0

15. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 0100100 A 2 0020021 A 3 0011013 T 4 0 A 5 0

16. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 0100100 A 2 0020021 A 3 0011013 T 4 0000201 A 5 0

17. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 0100100 A 2 0020021 A 3 0011013 T 4 0000201 A 5 0010031

18. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 0100100 A 2 0020021 A 3 0011013 T 4 0000201 A 5 0010031 TACTAA TAATA

19. 0 T1T1 A2A2 C3C3 T4T4 A5A5 A6A6 0 0000000 T 1 0100100 A 2 0020021 A 3 0011013 T 4 0000201 A 5 0010031 TACTAA TAATA

20. How do your prefer it – right or fast ? Exact methods - the result is guaranteed to be (mathematically) optimal  Needleman-Wunsch (global)  Smith-Waterman (local) Heuristic methods: make some assumptions that hold most, but not all of the time  FASTA  BLAST Still, a typical run takes minutes to complete.