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Bioinformatic Applications of Hidden Markov Models

MSCS230: Bioinformatics I. 2. Overview. The Dishonest Casino SolutionModel TrainingPairwise AlignmentProfile HMMs for sequence families, MSAGene Prediction. MSCS230: Bioinformatics I. 3. The Dishonest Casino. 1: 1/62: 1/63: 1/64: 1/65: 1/66: 1/6. 1: 1/102: 1/103: 1/104: 1/105: 1/106: 1

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Bioinformatic Applications of Hidden Markov Models

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    1. Bioinformatic Applications of Hidden Markov Models Craig A. Struble, Ph.D. Department of Mathematics, Statistics, and Computer Science Marquette University

    2. MSCS230: Bioinformatics I 2 Overview The Dishonest Casino Solution Model Training Pairwise Alignment Profile HMMs for sequence families, MSA Gene Prediction

    3. MSCS230: Bioinformatics I 3 The Dishonest Casino The dynamic programming algorithm is called the Viterbi algorithm.The dynamic programming algorithm is called the Viterbi algorithm.

    4. MSCS230: Bioinformatics I 4 Model Training Estimation of model parameters from data State sequence is known Count the number of times a transition/emission is taken Ratio of specific transition/emission vs. total transition/emission State sequence is unknown Baum-Welch [1972] Iterative procedure: initial estimate, consider probable paths, update parameters, repeat Expectation maximization

    5. MSCS230: Bioinformatics I 5 Pairwise Alignment Global alignment

    6. MSCS230: Bioinformatics I 6 Pairwise Alignment Local alignment

    7. MSCS230: Bioinformatics I 7 Profile HMMs HMM for consensus sequences

    8. MSCS230: Bioinformatics I 8 Profile HMMs

    9. MSCS230: Bioinformatics I 9 Profile HMM Applications Recognize structural elements Multiple sequence alignments Database searching

    10. MSCS230: Bioinformatics I 10 Gene Prediction GenScan C. Burge and S. Carlin, Prediction of complete gene structures in human genomic DNA, J Mol Biol 1997 Apr 25;268(1):78-94 Hidden Markov Model of gene structure http://genes.mit.edu/GENSCAN.html

    11. MSCS230: Bioinformatics I 11 GenScan The 3 to 5 side is the same, but all arrows are inverted. Legend F - 5 UTR T - 3 UTR N - intergenic region E - Exon I - Intron (these are split up based on the codon position of intron termination) The 3 to 5 side is the same, but all arrows are inverted. Legend F - 5 UTR T - 3 UTR N - intergenic region E - Exon I - Intron (these are split up based on the codon position of intron termination)

    12. MSCS230: Bioinformatics I 12 GenScan Training Trained on a set of annotated human genes Labeled state sequence What if you wanted use GenScan for mouse? rat? fish? yeast?

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