Lecture plan

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

1. Intro: Biology and CS, terminology and prerequisites.
2. Pairwise alignments
3. Local alignment, affine gap costs
4. Linear space alignments (pages 35-36). Scoring systems and models.
5. The multialignment problem. (Pages 135-149.)
6. Heuristics for local alignments. (Pages 33-34, Slides on Blast)
7. Protein clustering. NC score and what do we really want?
8. Mapping and filtering of DNA reads.
9. Hidden Markov Models for analyzing sequences.
10. More on HMMs
11. EM and HMM training
12. Phylogenetics. The parsimony method.
13. Distance methods, NJ
14. Tree reconciliation.