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Practical Bioinformatics Mark Voorhies 5/26/2015 Mark Voorhies Practical Bioinformatics Habits are things you get for free, without requiring any special work. Cory Doctorow Advice to Writers, 4/5/2012 Mark Voorhies Practical


  1. Practical Bioinformatics Mark Voorhies 5/26/2015 Mark Voorhies Practical Bioinformatics

  2. Habits are things you get for free, without requiring any special work. –Cory Doctorow Advice to Writers, 4/5/2012 Mark Voorhies Practical Bioinformatics

  3. Why compare sequences? Mark Voorhies Practical Bioinformatics

  4. EM: Training an HMM Mark Voorhies Practical Bioinformatics

  5. EM: Estimating transcript abundances Constrain Update parameters Online EM estimated counts algorithm A C G T A C + G T Error probabilities Bias Output Input ∝ λ ∝ α L Targets (i −1) c m i =m i c −1 i–1 Capture target Fragment and m � sequences sequence i Get next read pair Update masses Relative Estimated Effective abundances counts counts P (−) P (−) P (−) L Align to target references P ( ) ∝ λ L · ρ · ω p |−,L p Augmented · φ − | p, − ,L alignment file Calculate assignment probabilities Roberts and Pachter, Nature Methods 10:71 Mark Voorhies Practical Bioinformatics

  6. Evolution implies a self-consistent model Distances Topology (Pairwise relationships) (Evolutionary history) Mark Voorhies Practical Bioinformatics

  7. Measure all pairwise distances by dynamic programming Mark Voorhies Practical Bioinformatics

  8. Measure all pairwise distances by dynamic programming Mark Voorhies Practical Bioinformatics

  9. Generate a guide tree by UPGMA Mark Voorhies Practical Bioinformatics

  10. Generate a guide tree by UPGMA Mark Voorhies Practical Bioinformatics

  11. Generate a guide tree by UPGMA Mark Voorhies Practical Bioinformatics

  12. Generate a guide tree by UPGMA Mark Voorhies Practical Bioinformatics

  13. Generate a guide tree by UPGMA Mark Voorhies Practical Bioinformatics

  14. Progressive alignment following the guide tree Mark Voorhies Practical Bioinformatics

  15. Progressive alignment following the guide tree Mark Voorhies Practical Bioinformatics

  16. Progressive alignment following the guide tree Mark Voorhies Practical Bioinformatics

  17. Measure distances directly from the alignment Mark Voorhies Practical Bioinformatics

  18. Generate neighbor-joining tree from new distances Mark Voorhies Practical Bioinformatics

  19. Generate neighbor-joining tree from new distances Mark Voorhies Practical Bioinformatics

  20. Generate neighbor-joining tree from new distances Mark Voorhies Practical Bioinformatics

  21. Generate bootstrap values from subsets of the alignment Mark Voorhies Practical Bioinformatics

  22. Generating a multiple alignment in CLUSTALX Mark Voorhies Practical Bioinformatics

  23. Generating a multiple alignment in CLUSTALX Mark Voorhies Practical Bioinformatics

  24. Generating a neighbor joining tree in CLUSTALX Mark Voorhies Practical Bioinformatics

  25. Viewing the alignment and tree in JALVIEW Mark Voorhies Practical Bioinformatics

  26. Related tools Protein Multiple Alignment MUSCLE Clustal Omega Probcons hmmalign (HMMer3) Tree Building MrBayes (Bayesian MCMC) PhyML (maximum likelihood) RaxML (fast maximum likelihood) FastTree2 (very large heuristic trees) Mark Voorhies Practical Bioinformatics

  27. Homework Finish your dynamic programming implementation. Mark Voorhies Practical Bioinformatics

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