32 episodios

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.

Fundamental Algorithms in Bioinformatics UC Davis

    • Tecnología

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.

    • video
    Lecture 1: Introduction to bioinformatics and the course

    Lecture 1: Introduction to bioinformatics and the course

    Introduction to the course and bioinformatics. Why we do bioinformatics, how it relates to genomics
    and to the changing modalities of biology.

    • 3 min
    • video
    Lecture 2: Further introduction

    Lecture 2: Further introduction

    Continuation of the introduction to the course and
    to bioinformatics. The biological utility of the similarity of sequences. The first fact of bio-sequence
    analysis.

    • 4 min
    • video
    Lecture 3: Defining sequence similarity

    Lecture 3: Defining sequence similarity

    Definition of sequence similarity and string alignment,
    counting alignments the need for fast computation.

    • 4 min
    • video
    Lecture 4: Extending the model of sequence similarity

    Lecture 4: Extending the model of sequence similarity

    Review of the definition of sequence similarity and
    extensions of the model for greater biological fidelity.
    Introduction to parametric sequence alignment.

    • 4 min
    • video
    Lecture 5: Computing sequence similarity

    Lecture 5: Computing sequence similarity

    Introduction to computational efficiency. Introduction
    to how we actually compute sequence similarity efficiently.

    • 3 min
    • video
    Lecture 6: Computing similarity using an alignment graph

    Lecture 6: Computing similarity using an alignment graph

    Continuation of the discussion of how to efficiently
    compute the similarity of two sequences. Introduction
    to the traceback operation to find the
    optimal alignment.

    • 4 min

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