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. have an introductory understanding of Markov models of sequence evolution;
4. be familiar with the problems of sequence motif discovery, representation and recognition in a probabilistic framework;
5. understand the application of Hidden Markov Models in this framework, including the Viterbi, Forward, and Backward algorithms;
6. be introduced to the Baum Welch algorithm and the Gibbs Sampler;
7. 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;
8. 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 20th, 2011.
Maintained by Dannie Durand (durand@cs.cmu.edu).