Introduction to computational genomics a case study approach
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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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Introduction to computational genomics a case study approach

Introduction to Computational Genomics:a case study approach

CHAPTER 2

Gene Finding


Outline

OUTLINE

  • An introduction to genes and proteins

  • Gene finding

  • Hypothesis testing


Genes

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)


  • Proteins

    PROTEINS

    • Used in enzymes and as structural materials in cells

    • Chain of Amino Acid (AA)

    • Shape determines its function (protein folding)


    Aa alphabet

    AA ALPHABET

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


    Central dogma

    CENTRAL DOGMA


    Genetic code

    GENETIC CODE


    Open reading frame

    OPEN READING FRAME

    • Start condon (ATG = Methionine)

    • Non-stop condons

    • Stop condons (TGA, TAA, TAG)


    Gene finding

    GENE FINDING

    • Methods:

      • ab initio

      • homology based methods

    • Only prokaryotic genes consist of single continuous ORFs

    • Algorithm


    Lower bound

    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


    Definitions

    DEFINITIONS

    • Significance level

    • Test statistic

    • P-value

    • Types of errors

      • Type I error (false positive)

      • Type II error (false negative)


    Hypothesis testing

    HYPOTHESIS TESTING

    • Distinguish reliable patterns from background noise

    • Probability under null model

      • Significant when highly unlikely under null model


    Randomization test

    RANDOMIZATION TEST

    • Cannot easily calculate p-value

    • Randomization of observed data

    • Same statistical properties

      • Permutation

      • Bootstrapping


    Questions comments

    Questions/Comments?


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