1 / 14

Introduction to Computational Genomics: a case study approach

Introduction to Computational Genomics: a case study approach. CHAPTER 2 Gene Finding. OUTLINE. An introduction to genes and proteins Gene finding Hypothesis testing. GENES. Segment that specifies the sequence of a protein Exons = coding sequences Introns = non-coding sequences

thor-anderson
Download Presentation

Introduction to Computational Genomics: a case study approach

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction to Computational Genomics:a case study approach CHAPTER 2 Gene Finding

  2. OUTLINE • An introduction to genes and proteins • Gene finding • Hypothesis testing

  3. GENES • Segment that specifies the sequence of a protein • Exons = coding sequences • Introns = non-coding sequences • Occupies a specific location on a chromosome (an organized strand of DNA)

  4. PROTEINS • Used in enzymes and as structural materials in cells • Chain of Amino Acid (AA) • Shape determines its function (protein folding)

  5. AA ALPHABET A = {A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, V}

  6. CENTRAL DOGMA

  7. GENETIC CODE

  8. OPEN READING FRAME • Start condon (ATG = Methionine) • Non-stop condons • Stop condons (TGA, TAA, TAG)

  9. GENE FINDING • Methods: • ab initio • homology based methods • Only prokaryotic genes consist of single continuous ORFs • Algorithm

  10. LOWER BOUND • Uniform condon distribution • P(run of k non-stop condons) = (61/64)k • Non-uniform condon distribution • P(stop) = P(TAA) + P(TAG) + P(TGA) • P( run of k non-stop condons) = [1 – P(stop)]k

  11. DEFINITIONS • Significance level • Test statistic • P-value • Types of errors • Type I error (false positive) • Type II error (false negative)

  12. HYPOTHESIS TESTING • Distinguish reliable patterns from background noise • Probability under null model • Significant when highly unlikely under null model

  13. RANDOMIZATION TEST • Cannot easily calculate p-value • Randomization of observed data • Same statistical properties • Permutation • Bootstrapping

  14. Questions/Comments?

More Related