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Inicio / Buscador / Ciencia de Datos / Bioinformática
California Institute of Technology vía Coursera
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Finding Mutations in DNA and Proteins (Bioinformatics VI)

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  • Week 1: Introduction to Read Mapping

    • Welcome to our class! We are glad that you decided to join us.

      In this class, we will consider the following two central biological questions (the computational approaches needed to solve them are shown in parentheses):

      1. How Do We Locate Disease-Causing Mutations? (Combinatorial Pattern Matching)
      2. Why Have Biologists Still Not Developed an HIV Vaccine? (Hidden Markov Models)

      As in previous courses, each of these two chapters is accompanied by a Bioinformatics Cartoon created by talented artist Randall Christopher and serving as a chapter header in the Specialization's bestselling print companion. You can find the first chapter's cartoon at the bottom of this message.

  • Week 2: The Burrows-Wheeler Transform

    • Welcome to week 2 of the class!



      This week, we will introduce a paradigm called the Burrows-Wheeler transform; after seeing how it can be used in string compression, we will demonstrate that it is also the foundation of modern read-mapping algorithms.

  • Week 3: Speeding Up Burrows-Wheeler Read Mapping

    • Welcome to week 3 of class!



      Last week, we saw how the Burrows-Wheeler transform could be applied to multiple pattern matching. This week, we will speed up our algorithm and generalize it to the case that patterns have errors, which models the biological problem of mapping reads with errors to a reference genome.

  • Week 4: Introduction to Hidden Markov Models

    • Welcome to week 4 of class!



      This week, we will start examining the case of aligning sequences with many mutations -- such as related genes from different HIV strains -- and see that our problem formulation for sequence alignment is not adequate for highly diverged sequences.



      To improve our algorithms, we will introduce a machine-learning paradigm called a hidden Markov model and see how dynamic programming helps us answer questions about these models.

  • Week 5: Profile HMMs for Sequence Alignment

    • Welcome to week 5 of class!



      Last week, we introduced hidden Markov models. This week, we will see how hidden Markov models can be applied to sequence alignment with a profile HMM. We will then consider some advanced topics in this area, which are related to advanced methods that we considered in a previous course for clustering.

  • Week 6: Bioinformatics Application Challenge

    • Welcome to the sixth and final week of class!



      This week brings our Application Challenge, in which we apply the HMM sequence alignment algorithms that we have developed.