## 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;
- 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;
- 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.

Last modified: August 30th, 2021.

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