Lecture plan

This is a tentative lecture plan, and subject to change.

* Intro: Biology and CS, terminology and prerequisites.
* Pairwise alignments
* Local alignment, affine gap costs
* Linear space alignments (pages 35-36), Affine gap scores. Scoring systems and models.
* The multialignment problem. (Pages 135-149.)
* Heuristics for local alignments. (Pages 33-34.)
* Protein clustering. NC score and what do we really want?
* Mapping and filtering of DNA reads.
* Hidden Markov Models for analyzing sequences.
* More on HMMs
* EM and HMM training
* Phylogenetics. The parsimony method.
* Distance methods, NJ
* Tree reconciliation.