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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

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Introduction to Computational Genomics: a case study approach

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  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?

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