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. The multialignment problem. (Pages 135-149.)
5. Heuristics for local alignment (pages 33-34, Slides on Blast) and the Burrows-Wheeler transform for fast searches
6. Protein clustering: NC score and what do we really want? Filtering of DNA reads
7. Hidden Markov Models for analyzing sequences.
8. More on HMMs
9. EM and HMM training
10. Phylogenetics. The parsimony method.
11. Distance methods, NJ
12. Tree reconciliation.
13. Genome assembly
14. More on genome assembly