Week 
Topics 
References 
Assignments 
14 
RNA structure: why
structure is important? Dynamic programming algorithm for RNA structure
prediction. Inconsistency of maximum parsimony. Introduction to RNA. 
Sect. 10.1 and 10.2 (p. 265273) Sect. 8.6. Sect. 10.1 (p.262265) 

13 
Maximum likelihood (cont.).
Compare phylogenetic methods: when is maximum parsimony justified? Maximum likelihood inference of phylogeny. 
Sect. 8.4 (p.206207),
Sect. 8.6 (not fully covered) Sect. 8.3. 

12 
Probabilistic models of
evolution. Compatibility and perfect phylogeny. 
Sect. 8.1, 8.2. Notes by Gusfield (PDF). 
HW4 
11 
Parsimony: Fitch and
Sankoff algorithms, branch and bound. Neighour Joining: why it finds the right tree? 
Sect. 7.4. Sect. 7.3. The proof I presented in class is based on this paper. 
Project 2 
10 
Ultrametric trees and
additive trees. Algorithms for inferrence when data is perfect. Phylogeny: introduction and counting. 
Chap. 7: p.166170. If you have
Gusfield's book, you may also read Sect. 17.1, 17.2 and 17.4.1. Chap. 7: p. 161165. 

9 
MSA with profile HMM. Star
alignment approximation. A little of progressive alignments. Discussion of project 1. MSA: branch and bound. 
Chap. 6: p. 145157. See Gusfield's book, Sect. 14.6.2. if you have it. Otherwise, you can read the paper by Gusfield.. Chap. 6: p.143. 
HW3 
8 
Profile HMM (cont.). MSA:
scoring and dynamic programming. Profile HMM 
Chap. 5: sect. 5.5 and 5.7.
Chap. 6: p.135143. Chap. 5: sect. 5.15.3. 

7 
Pairwise alignment with HMM. EM and BaumWelch. More on HMM. 
Chap. 4. Sect. 3.43.5: p. 6973. Sect. 11.6. 
New
test data Note: read the READMEnew file carefully. 
6 
10/8: HMM parameter
estimation: BaumWelch algorithm. 10/6: Algorithms for HMM: Viterbi, Forward/Backward. Numerical issues. 
Sect. 3.3. Also p. 312313. Sect. 3.2 (p.5662) and Sect. 3.6. 

5 
10/1: Markov models. Hidden
Markov models: what is hidden? 9/29: Significance of alignment scores 
Sect. 3.13.2 (p. 4755) Sect. 2.7 (also Sect. 11.1) 
HW2 
4 
9/24: Linear space sequence
alignment. A little bit on Blast. 9/22: MUM revisited. Repeated matches. More complex gap penalty models. 
The spacesaving algorithm
explained by Gusfield (PDF). Also read
Sect. 2.6. p. 2526, and Sect. 2.4. 
Project 1 Test data 
3 
9/17: Local alignment:
SmithWaterman algorithms and expected score of random matches. Overlap
matches. 9/15: Pairwise sequence alignment. Statistical justification of the scoring model. 
Sect. 2.3: p.22p.25, p.27. Sect. 21.  2.3 (up to p. 22) 

2 
9/10: Search pattern
efficiently in suffix array. Two applications of suffix tree. A little
bit on MUMs. 9/8: Algorithm for building suffix tree. Application in text compression. 
My notes on suffix tree and
suffix array (PDF). It is updated with a
section on pattern search in suffix array. My notes on two applications we discussed today (PDF). If you want, you can also read the original paper on lineartime suffix array algorithm (PDF). 
HW1 Updated 9/15 to fix an offbyone error in Problem 3 
1 
9/3 Suffix
tree and suffix
array algorithms. 9/1: Introduction of bioinformatics, Exact string matching: a simple linear time method 
An
introduction to suffix
tree by Dan Gusfield (PDF). A simple introduction to suffix array link. Explanation of lineartime algorithm of LCP array construction by Dan Gusfield (PDF). Note it gives argument for the claim I made but did not prove. Explanation of the Zalgorithm by Gusfield (PDF). 