1. Biocomputation: Comparative Genomics Tanya Talkar Lolly Kruse Colleen O’Rourke

2. DNA

5. What is Biocomputation? Statistics Computer Science Molecular Biology

6. Four Main Parts  Biomolecular computation  Biological Computation  Computational Biology  Bioinformatics

7. Bioinformatics: Biology Computer Science Information Technology

8. Sequence Analysis  Very Functional!  Compare DNA between Species  Small Fragments  Return full sequence

9. Computational Genomics  Needleman – Wunsch  Not used much  More Mapped Genomes = Computational Genomics!

10. Alignment

11. Global Alignment: Needleman - Wunsch  O(N 3 )  Fewest edit operations  Similar strings

12. Local Alignment Smith - Waterman  O(N 2 )  Dissimilar strings  Find high similarity regions

13. Comparison

14. S1PQRAXABCSTVQ S2XYAXBACSLT

15. S1AXAB_CS S2AX_BACS Score 2 2 -1 2 -1 2 2

16. Advantages: Global Alignment

17. Advantages: Local Alignment

18. BLAST Basic Local Alignment Search Tool FASTA

19. Improvements  Increased Speed  Locate initial alignment hot spots  Statistical significance

20. Terminology  Segment Pairs  Locally maximal segment pairs  Maximal segment pairs

21. How it works  Query sentence, P  Database  Must have score over C!  Multiple segment pairs combined ABCDEFG AGCBFDE BEDGAFB GFBEDCA

22. How it works  Extends each hit  Done efficiently  Truncates  Doesn’t find all pairs

23. Proteins  Fixed length, W  Words above threshold  Each hit extended

24. DNA  Word List  Exact matches  NOT dynamic programming

25. Scoring  Blosum62 Matrix  Match (+2), Mismatch (-3), Gaps penalized

26. Substitution Matrix  Represents Scoring Functions

27. Multiple Sequence Alignment

28. Methods of MSA  Progressive Alignment Construction  Iterative Methods  Hidden Markov Models  Genetic Algorithms and Simulated Annealing

29. Comparative Genomics  Compare Species  Find Evolutionary Significances!  Low Level  High Level  Importance of Non Coding DNA