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D00922025 黃任鋒 R00922102 張庭耀 R00922156 陳子筠 R99922158 蘇宏麒

Protein multiple sequence alignment by hybrid bio-inspired algorithms Vincenzo Cutello, Giuseppe Nicosia*, Mario Pavone and Igor Prizzi Nucleic Acids Research, 2011. D00922025 黃任鋒 R00922102 張庭耀 R00922156 陳子筠 R99922158 蘇宏麒. 1. Outline. Introduction & background IMSA

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D00922025 黃任鋒 R00922102 張庭耀 R00922156 陳子筠 R99922158 蘇宏麒

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  1. Protein multiple sequence alignment by hybrid bio-inspired algorithmsVincenzo Cutello, Giuseppe Nicosia*, Mario Pavone and Igor PrizziNucleic Acids Research, 2011 • D00922025 黃任鋒 • R00922102 張庭耀 • R00922156 陳子筠 • R99922158 蘇宏麒 1

  2. Outline • Introduction & background • IMSA • Cloning and hypermutation operators • Results • Conclusion

  3. Introduction and BackgroundD00922025 黃任鋒 3

  4. About this paper 4

  5. Problem of MSA 5

  6. Methods for MSA 6

  7. Progressive alignments 7

  8. Exact algorithms 8

  9. Survey of MSA 9

  10. Outline • Introduction & background • IMSA • Cloning and hypermutation operators • Results • Conclusion

  11. Immunological Multiple Sequence Alignment(IMSA)R00922102 張庭耀 11

  12. IMSA • Two different strategies to create the initial population • New hypermutation operators • solving protein MSA that insert or remove gaps • Gap columns, which have been matched, are moved to the end of the sequence • The remaining elements(i.e. amino acids) and existing gaps are shifted into the freed space 12

  13. IMSA • Considers antigens (Ags) and B cells • Ag is a given MSA instance, i.e. the protein sequences to align • B cells are a population of alignments that have solved(or approximated) the initial problem 13

  14. 14

  15. 15

  16. Initial population strategies 16

  17. Random_initialization 17

  18. Random_initialization 18

  19. CLUSTALW-seeding 19

  20. Outline • Introduction & background • IMSA • Cloning and hypermutation operators • Results • Conclusion

  21. IMSA-Cloning and hypermutation operatorsR00922156 陳子筠 21

  22. Cloning and hypermutation operators • Represented by “Static cloning operators” • Clones B cells dup times • P(clo) of Nc = d * dup B cells, d is population size 22

  23. Cloning and hypermutation operators 23

  24. InsGap 24

  25. InsGap P(gap) 25

  26. RemGap P(gap) 26

  27. BlockShuffling operator • Select randomly start point in a sequence • BlockMove • BlockSplitHor • BlockSplitVer 27

  28. BlockMove P(block) 28

  29. BlockSplitHor P(block) 29

  30. BlockSplitVer P(block) 30

  31. STRIP_GAPS 31

  32. Aging operator • Eliminates old B cells in populations P(t), P(gap) and P(block) • The generation number of B cell is τB • New population P(t+1) of d B cells selected best survivors by (μ+λ) - selection 32

  33. 33

  34. Outline • Introduction & background • IMSA • Cloning and hypermutation operators • Results • Conclusion

  35. ResultsR99922158 蘇宏麒 35

  36. Classical Benchmark • BAliBASE version 1.0, 2.0 and 3.0 • A benchmark alignment database . • The evaluation of multiple sequence alignment. 36

  37. BAliBASE version 1.0 • 141 reference alignments • 5 reference sets 37

  38. BAliBASE version 1.0, cont. • Reference 1: equi-distant sequences with various levels of conservation • Reference 2: family aligned with a highly divergent “orphan” sequence • Reference 3: subgroups with < 25% residue identity between groups • Reference 4: sequences with N/C-terminal extensions • Reference 5: internal insertion 38

  39. BAliBASE version 2.0 • Include all alignments in version 1.0 • Alignments are verified and corrected 39

  40. BAliBASE version 3.0 • same as version 2.0 • contains 218 alignments 40

  41. IMSA - reference 1 lad2 41

  42. IMSA - reference 1 laym3 42

  43. IMSA - reference 1 1hfh 43

  44. IMSA - reference 1 2mhr 44

  45. IMSA - Reference 3 luky 45

  46. IMSA - Reference 5 1qpg 46

  47. IMSA - BAliBASE 1.0 47

  48. IMSA vs CLUSTALW-seeding - BAliBASE 1.0 48

  49. IMSA - BAliBASE 2.0 49

  50. IMSA vs CLUSTALW-seeding - BAliBASE 2.0 50

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