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    Technology

    Fundamental Algorithms in Bioinformatics

    This course covers fundamental algorithms for efficient analysis of biological sequences and for building evolutionary trees. This is an undergraduate course focusing on the ideas and concepts behind the most central algorithms in biological sequence analysis. Dynamic Programming, Alignment, Hidden Markov Models, Statistical Analysis are emphasized.

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    Copyright: © Copyright The Regents of the University of California, Davis campus, 2015. All Rights Reserved.

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    Latest Episodes:
    Lecture 29; Additive trees and the Neighbor-Joining algorithm Jan 30, 2010
    Show notes

    Additive trees and their construction. The Neighbor-Joining algorithm and its use with near-additive data. Bootstrap values and their misuse.


    Lecture 28: Algorithms for Ultrametric trees — molecular clocks Jan 29, 2010
    Show notes

    Algorithms for constructing an Ultrametric Tree from an Ultrametric Matrix, and the relationship of ultrametrics to the molecular clock.


    Lecture 27: Introduction to evolutionary trees - Ultrametric trees Jan 28, 2010
    Show notes

    lntroduction to trees that represent evolution. We start with the case of perfect data: the Ultrametric tree case.


    Lecture 26: Hidden Markov models - The Backwards algorithm Jan 27, 2010
    Show notes

    What the Backwards algorithm computes and why we want it. Profile HMMs and their use. Cleaning up some topics in sequence analysis (running out of time); PSI-BLAST and its dangers.


    Lecture 25: From the Vitterbi algorithm to the forward algorithm Jan 26, 2010
    Show notes

    This class finishes the discussion of the Vitterbi algorithm, its time analysis and the traceback algorithm. Introduction to the Forward algorithm to compute the probability that a given sequence is generate by the HMM.


    Lecture 24: Hidden Markov models and the Vitterbi algorithm Jan 25, 2010
    Show notes

    Finish the discussion of HMMs for CpG islands. Introduction to the Vitterbi algorithm (really dynamic programming) to find the most likely Markov Chain generating a given sequence.


    Lecture 23: Hidden Markov models Jan 24, 2010
    Show notes

    Hidden Markov models to identify CpG islands. This lecture follows the discussion in Durbin and Eddy.


    Lecture 22: From profiles to Markov models Jan 23, 2010
    Show notes

    Finish the discussion of profiles and log-odds ratios. introduction to Markov Models and Hidden Markov Models


    Lecture 21: Uses of multiple sequence alignment Jan 22, 2010
    Show notes

    Use of multiple sequence alignment to build a model of a set of biologically related sequences. Profiles, log-odds ratios.


    Lecture 20: Multiple sequence alignment III Jan 21, 2010
    Show notes

    The center tree method and analysis; progressive alignment, guide trees, CLUSTAL, uses of multiple alignment


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