Computational Molecular Biology and Genomics

Learning objectives

After completing this course, students will

  1. have a working knowledge of algorithms for global, semi-global, and local alignment and be able apply those algorithms to concrete examples;
  2. understand how parameter selection in a scoring function influences the results obtained using an alignment algorithm;
  3. be familiar with the three basic families of phylogeny reconstruction methods: maximum parsimony, distance, and maximum likelihood and be able to select an appropriate method for a given data set;
  4. have an introductory understanding of Markov models of sequence evolution;
  5. be familiar with the problems of sequence motif discovery, representation and recognition in a probabilistic framework;
  6. understand the application of Hidden Markov Models in this framework, including the Viterbi, Forward, and Backward algorithms;
  7. be introduced to the Baum Welch algorithm and the Gibbs Sampler;
  8. understand the purpose of amino acid substitution matrices that are parameterized by evolutionary distance and be introduced to the derivation of the PAM substitution matrices from a Markov model of sequence evolution;
  9. have an in depth knowledge of the BLAST database search heuristic and its parameters; be able to select appropriate parameter values for a given query sequence and retrieval goal; and be able to interpret the statistical output of BLAST.

Last modified: August 30th, 2021.
Maintained by Dannie Durand (