||RNA structure: why
structure is important? Dynamic programming algorithm for RNA structure
Inconsistency of maximum parsimony. Introduction to RNA.
|Sect. 10.1 and 10.2 (p. 265-273)
Sect. 8.6. Sect. 10.1 (p.262-265)
||Maximum likelihood (cont.).
Compare phylogenetic methods: when is maximum parsimony justified?
Maximum likelihood inference of phylogeny.
|Sect. 8.4 (p.206-207),
Sect. 8.6 (not fully covered)
||Probabilistic models of
Compatibility and perfect phylogeny.
|Sect. 8.1, 8.2.
Notes by Gusfield (PDF).
||Parsimony: Fitch and
Sankoff algorithms, branch and bound.
Neighour Joining: why it finds the right tree?
Sect. 7.3. The proof I presented in class is based on this paper.
||Ultrametric trees and
additive trees. Algorithms for inferrence when data is perfect.
Phylogeny: introduction and counting.
|Chap. 7: p.166-170. If you have
Gusfield's book, you may also read Sect. 17.1, 17.2 and 17.4.1.
Chap. 7: p. 161-165.
||MSA with profile HMM. Star
alignment approximation. A little of progressive alignments.
Discussion of project 1. MSA: branch and bound.
|Chap. 6: p. 145-157.
See Gusfield's book, Sect. 14.6.2. if you have it. Otherwise, you can read the paper by Gusfield..
Chap. 6: p.143.
||Profile HMM (cont.). MSA:
scoring and dynamic programming.
|Chap. 5: sect. 5.5 and 5.7.
Chap. 6: p.135-143.
Chap. 5: sect. 5.1-5.3.
||Pairwise alignment with HMM.
EM and Baum-Welch. More on HMM.
Sect. 3.4-3.5: p. 69-73. Sect. 11.6.
Note: read the README-new file carefully.
||10/8: HMM parameter
estimation: Baum-Welch algorithm.
10/6: Algorithms for HMM: Viterbi, Forward/Backward. Numerical issues.
|Sect. 3.3. Also p. 312-313.
Sect. 3.2 (p.56-62) and Sect. 3.6.
||10/1: Markov models. Hidden
Markov models: what is hidden?
9/29: Significance of alignment scores
|Sect. 3.1-3.2 (p. 47-55)
Sect. 2.7 (also Sect. 11.1)
||9/24: Linear space sequence
alignment. A little bit on Blast.
9/22: MUM revisited. Repeated matches. More complex gap penalty models.
|The space-saving algorithm
explained by Gusfield (PDF). Also read
p. 25-26, and Sect. 2.4.
||9/17: Local alignment:
Smith-Waterman algorithms and expected score of random matches. Overlap
9/15: Pairwise sequence alignment. Statistical justification of the scoring model.
|Sect. 2.3: p.22-p.25, p.27.
Sect. 21. - 2.3 (up to p. 22)
||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 linear-time suffix array algorithm (PDF).
to fix an off-by-one error in Problem 3
tree and suffix
9/1: Introduction of bioinformatics,
Exact string matching: a simple linear time method
introduction to suffix
tree by Dan Gusfield (PDF).
A simple introduction to suffix array link.
Explanation of linear-time 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 Z-algorithm by Gusfield (PDF).