03511 - Objectives
## Computational Molecular Biology and Genomics - Learning Objectives

After completing this course, students will

- have a working knowledge of algorithms for global,
semi-global, and local alignment and be able apply those algorithms to
concrete examples;
- understand how parameter selection in a scoring function
influences the results obtained using an alignment algorithm;
- have an introductory understanding of Markov models of
sequence evolution;
- be familiar with the problems of sequence motif
discovery, representation and recognition in a probabilistic framework;
- understand the application of Hidden Markov Models in
this framework, including the Viterbi, Forward, and Backward algorithms;
- be introduced to the Baum Welch algorithm and the Gibbs
Sampler;
- 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;
- 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.

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Last modified: August 20th, 2011.

Maintained by Dannie Durand (durand@cs.cmu.edu).