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Gene Prediction

Gene Prediction. Chengwei Luo, Amanda McCook, Nadeem Bulsara, Phillip Lee, Neha Gupta, and Divya Anjan Kumar. Gene Prediction. Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema. Gene Prediction. Introduction

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Gene Prediction

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  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 • Introduction • 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 New sequencing technology Metagenomics: ~1% grow in lab

  6. How to do it?

  7. How to do it? 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 Homology Search Phillip Lee & Divya Anjan Kumar 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

  13. Homology Search

  14. Strategy

  15. open reading frame(ORF)

  16. How/Why find ORF?

  17. How/Why find ORF?

  18. 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

  35. GeneMark.hmm

  36. GeneMarkS

  37. Glimmer

  38. Glimmer Journey

  39. Glimmer3.02

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

  41. Features

  42. 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

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