1. Gene Prediction Chengwei Luo, Amanda McCook, Nadeem Bulsara, Phillip Lee, Neha Gupta, and Divya Anjan Kumar

2. Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

3. Gene Prediction IntroductionIntroduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

4. Why gene prediction? experimental way?

5. Why gene prediction? Exponential growth of sequences Metagenomics: ~1% grow in lab New sequencing technology

6. How to do it?

7. It is a complicated task, let’s break it into parts

8. How to do it? It is a complicated task, let’s break it into parts Genome

9. How to do it? It is a complicated task, let’s break it into parts Genome

10. How to do it? Protein-coding gene prediction Phillip Lee & Divya Anjan Kumar Homology Search ab initio approach Nadeem Bulsara & Neha Gupta

11. How to do it? RNA gene prediction Amanda McCook & Chengwei Luo tRNA rRNA sRNA

12. Homology Search

14. Strategy

15. open reading frame(ORF)

16. How/Why find ORF?

19. Protein Database Searches

20. Domain searches

21. Limits of Extrinsic Prediction

22. ab initio Prediction

23. Homology Search is not Enough! Biased and incomplete Database sequenced genomes are not evenly distributed on the tree of life, and does not reflect the diversity accordingly either.

24. ab initio Gene Prediction

25. Features

26. ORFs (6 frames)

27. Codon Statistics

28. Features (Contd.)

29. Probabilistic View

30. Supervised Techniques

31. Unsupervised Techniques

32. Usually Used Tools GeneMark Glimmer EasyGene PRODIGAL

33. GeneMark

34. GeneMark.hmm

36. GeneMarkS

37. Glimmer

38. Glimmer Journey

39. Glimmer3.02

40. PRODIGAL Prokaryotic Dynamic Programming Gene Finding Algorithm Developed at Oak Ridge National Laboratory and the University of Tennessee

41. Features

43. EasyGene Developed at University of Copenhagen Statistical significance is the measure for gene prediction.

44. Comparison of Different Tools

45. RNA Gene Prediction

46. Why Predict RNA?

47. Regulatory sRNA

48. sRNA Challenges

49. Fundamental Methodology

50. RFAM

51. What Is Covariance? Fig: Christian Weile et al. BMC Genomics (2007) 8:244

52. Noncomparative Prediction Fig: James A. Goodrich & Jennifer F. Kugel, Nature Rev. Mol. Cell Biol. (2006) 7:612

53. Noncomparative Prediction *Rolf Backofen & Wolfgang R. Hess, RNA Biol. (2010) 7:1

54. Comparative+Noncomparative Effective sRNA prediction in V. cholerae Non-enterobacteria sRNAPredict2 32 novel sRNAs predicted 9 tested 6 confirmed Jonathan Livny et al. Nucleic Acids Res. (2005) 33:4096

55. Software *Rolf Backofen & Wolfgang R. Hess, RNA Biol. (2010) 7:1 Eva K. Freyhult et al. Genome Res. (2007) 17:117

56. Modification & finishing Consensus strategy to integrate ab initio results Broken gene recruiting TIS correcting IS calling operon annotating Gene presence/absence analysis

57. Modification & finishing Consensus strategy pass fail Broken gene recruiting ab initio results homology search candidate fragments

58. Modification & finishing TIS correcting Start codon redundancy:ATG, GTG, TTG, CTG Markov iteration, experimental verified data Leaderless genes

59. Modification & finishing IS callingOperon annotating IS Finder DB

60. Modification & finishing Gene Presence/absence analysis

61. Schema (proposed)

62. assembly group

63. Schema (proposed) assembly group