Advanced ComputationAL Biology Project Presentation

1. Team Members: Joshua Wu 11174269 Shuyu (Christine) Xu 11161640 Advanced ComputationAL Biology Project Presentation

2. OVERVIEW Project Description Introduction Motivation Bioinformatics Application Explicit vs Implicit Problem Analysis Implement Files Experimental Results Conclusion Possible Future Work Now we are here

3. Project Description Explicit Suffix Trees Suppose that we want to store explicitly all strings that are edge labels of a suffix tree. The main question of this project is how much space explicit suffix trees require comparing to implicit suffix trees. Implement suffix tree algorithm and run it on substrings of real data.

4. OVERVIEW Project Description Introduction Motivation Bioinformatics Application Explicit vs Implicit Problem Analysis Implement Files Experimental Results Conclusion Possible Future Work Now we are here

5. Introduction • Any string of length m can be degenerated into m suffixes, and these suffixes can be stored in a suffix tree. • Setup time O(m) (m is length of string) • searching time O(n) (n is length of pattern)

6. OVERVIEW • Project Description • Introduction • Motivation • Bioinformatics Application • Explicit vs Implicit • Problem Analysis • Implement Files • Experimental Results • Conclusion • Possible Future Work Now we are here

7. Motivation • "Suffix trees are widely used in the computer field... Recent improvements in the method have cut the memory requirement to 17 bytes per letter, which brings the method to the verge of practicality [for bioinformatics applications]" -- Nat Goodman (Genome Technology).

8. OVERVIEW • Project Description • Introduction • Motivation • Bioinformatics Application • Explicit vs Implicit • Problem Analysis • Implement Files • Experimental Results • Conclusion • Possible Future Work Now we are here

9. Bioinformatics Application • multiple genome alignment (Michael Hohl et al., 2002) • selection of signature oligonucleotides for DNA arrays (Kaderali and Schliep, 2002) • identification of sequence repeats (Kurtz and Schleiermacher, 1999)

10. OVERVIEW • Project Description • Introduction • Motivation • Bioinformatics Application • Explicit vs Implicit • Problem Analysis • Implement Files • Experimental Results • Conclusion • Possible Future Work Now we are here

11. Explicit vs Implicit • ABC $ Explicit • 1 2 3 4 ABC$ $ BC$ C$ Implicit 1,4 4,4 2,4 3,4

12. OVERVIEW • Project Description • Introduction • Motivation • Bioinformatics Application • Explicit vs Implicit • Problem Analysis • Implement Files • Experimental Results • Conclusion • Possible Future Work Now we are here

13. Problem Analysis • Best Case for explicit and implicit suffix trees: All different characters • Best case not likely with DNA inputs: total of 4 characters • Worst case: same characters throughout

14. Assumptions • In implicit trees, each number will only take up one bit. (the number 10 takes up 1 bit) • Only alphabets will be in the sequence

15. Example: all different char • ABCD $ 1,5 5,5 • 1 2 3 4 5 2,5 3,5 4,5 • N: string length • N = 5 • Memory = 10 • best case

16. Example • ABCABC $ 7,7 • 1 2 3 4 5 6 7 1,3 2,3 6,6 • N: string length • N = 7 4,7 7,7 7,7 7,7 • Memory = 20 4,7 4,7

17. Example: all same character • AAAA $ • 1 2 3 4 5 1,1 5,5 • N=string length • N = 5, 6, 7 2,2 5,5 • Memory = 16, 20, 24 • Memory = 4n-4 3,3 5,5 • Worse case 4,5 5,5

18. Program Input Data DNA for all kinds of creatures: Homo Sapiens, Monkeys, Chickens, …

19. OVERVIEW • Project Description • Introduction • Motivation • Bioinformatics Application • Explicit vs Implicit • Problem Analysis • Implement Files • Experimental Results • Conclusion • Possible Future Work Now we are here

20. Sample input: Homo Sapien • cagctcctgagactgctggcatgaaggggagccgtgccctcctgctggtggccctcaccctgttctgcatctgccggatggccacaggggaggacaacgatgagtttttcatggacttcctgcaaacactactggtggggaccccagaggagctctatgaggggaccttgggcaagtacaatgtcaacgaagatgccaaggcagcaatgactgaactcaagtcctgcagagatggcctgcagccaatgcacaaggcggagctggtcaagctgctggtgcaagtgctgggcagtcaggacggtgcctaagtggacctcagacatggctcagccataggacctgccacacaagcagccgtggacacaacgcccactaccacctcccacatggaaatgtatcctcaaaccgtttaatcaataa

21. Sample result

22. Sample input 2: plants • EARPIVVGPPPPLSGGLPGTENSDQARDGTLPYTKDRFYLQPLPPTEAAQRAKVSASEILNVKQFIDRKAWPSLQNDLRLRASYLRYDLKTVISAKPKDEKKSLQELTSKLFSSIDNLDHAAKIKSPTEAEKYYGQTVSNINEVLAKLG

23. Sample output:

24. OVERVIEW • Project Description • Introduction • Motivation • Bioinformatics Application • Explicit vs Implicit • Problem Analysis • Implement Files • Experimental Results • Conclusion • Possible Future Work Now we are here

25. Homo Sapien

26. Sample Input: Homo Sapiens • atgaaggggagccgtgccctcctgctggtggccctcaccctgttctgcatctgccggatggccacaggggaggacaacgatgagtttttcatggacttcctgcaaacactactggtggggaccccagaggagctctatgaggggaccttgggcaagtacaatgtcaacgaagatgccaaggcagcaatgactgaactcaagtcctgcagagatggcctgcagccaatgcacaaggcggagctggtcaagctgctggtgcaagtgctgggcagtcaggacggtgcctaa

27. Comparisons: Homo Sapiens

28. Comparisons: Homo Sapiens

29. Monkey Virus

30. Sample Input: Monkey Virus • GGSCFKCGKKGHFAKNCHEHAHNNAEPKVPGLCPRCKRGKHWANECKSKTDNQGNPIPPH

31. Monkey Virus

32. Plants

33. Sample Input: Plants • EARPIVVGPPPPLSGGLPGTENSDQARDGTLPYTKDRFYLQPLPPTEAAQRAKVSASEILNVKQFIDRKAWPSLQNDLRLRASYLRYDLKTVISAKPKDEKKSLQELTSKLFSSIDNLDHAAKIKSPTEAEKYYGQTVSNINEVLAKLG

34. Plants

35. Tobacco

36. Sample input: tobacco • SYSITTPSQFVFLSSAWADPIELINLCTNALGNQFQTQQARTVVQRQFSEVWKPSPQVTVRFPDSDFKVYRYNAVLDPLVTALLGAFDTRNRIIEVENQANPTTAETLDATRRVDDATVAIRSAINNLIVELIRGTGSYNRSSFESSSGLVWTSGPAT

37. Tobacco

38. Insects

39. Sample Input: Insects • DCLSGRYKGPCAVWDNETCRRVCKEEGRSSGHCSPSLKCWCEGC

40. Insects

41. Birds

42. Sample Input: Birds • IDTCRLPSDRGRCKASFERWYFNGRTCAKFIYGGCGGNGNKFPTQEACMKRCAKA

43. Birds

44. SARS

45. Sample Input: SARS • ALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEV

46. SARS

47. Fish

48. Sample Input: Fish • GHHHHHHLEDPSGGTPYIGSKISLISKAEIRYEGILYTIDTENSTVALAKVRSFGTEDRPTDRPIAPRDETFEYIIFRGSDIKDLTVCEPPKPIM

49. Fish

50. Chicken