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Introduction to Computational Genomics: a case study approach

This chapter provides an introduction to gene finding in computational genomics, covering the basic concepts of genes, proteins, and their structures. It discusses the significance of coding sequences (exons) and non-coding sequences (introns), as well as the central dogma of molecular biology. The chapter explores methods of gene finding, including ab initio and homology-based approaches, and elaborates on statistical hypothesis testing, significance levels, and methods to distinguish reliable genetic patterns from background noise. Case studies illustrate practical applications.

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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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