Week 
Topics 
References 
Assignments 
14 
Course project presentation 

13 
Lecture 26: RNA. Lecture 25: Transformational grammars. 
Chapters 9 and 10. 

12 
Lecture 24: Neighbor joining. Parsimony. Lecture 23: Distance methods for inferring phylogeny. 
Chapter 7. 
HW5. Due: 11/30. 
11 
Lecture 22: Introduction to phylogenetics. Lecture 21: Profile HMM. Multiple sequence alignment with HMM. 
Chapter 6. 
HW4. Due: 11/16. 
10 
Lecture 20: Profile HMM.
Weighting of sequences. Lecture 19: Profile HMM. Pseudo counts. 
Chapter 5. 

9 
Lecture 18: Profile HMM. The
issue of pseudocount. Lecture 17: Profile. Profile HMM. 
Chapter 5. And also parts of
Chapter 11 (page 303 and Sect. 11.5) 

8 
Lecture 16: Pair HMM. Lecture 15: Other issues with HMM. Pair HMM. 
Chapters 4 and 5. Lecture
notes on profile. 
Project proposal. Due: 10/27. Description of course project. 
7 
Lecture 14: Parameter estimation using
BaumWelch algorithm. EM algorithm. Model structure. Lecture 13: HMM: forward/backward algorithms. Posterior decoding. 
Chapter 3. 
Programming
assignment. Due: 11/3.
Note: deadline extended. 
6 
Lecture 12: HMM. Viterbi
algorithm. Lecture 11: Markov model. Hidden Markov Model. 
Chapter 3. 

5 
Lecture 10: Probabilistic
model and Markov chain. Lecture 9: Multiple sequence alignment. 
Lecture notes by Gusfield (in
HuskyCT). Sections 11.2 and 11.3. Chapter 3. 
HW3.
Due: 10/10. Note this is a Tuesday. 
4 
Lecture 8: Multiple sequence
alignment. Lecture 7: Extreme value distribution and sequence alignment. Multiple sequence alignment. 
Section 11.1. Lecture notes
by Gusfield (see HuskyCT). 

3 
Lecture 6: PAM. Database
search. A quick introduction to Blast. Statistical
significance of matches. Lecture 5: Gaps, PAM scoring matrix. 
Chapter 2 (2.4 to 2.8). Also
I will post lecture notes in HuskyCT. For background on
probability, read Section 11.1 of the textbook. 
HW2.
Due: Sept. 28, end of day. Submit in HuskyCT. 
2 
Lecture 4: Alignment in
linear space. Variations of alignments Lecture 3: Sequence alignment: traceback, local alignment. 
Chapter 2 and lecture notes. Gusfield's notes on linear space alignment. 

1 
Lecture 2:
Sequence alignment with dynamic programming. Lecture 1: Introduction of bioinformatics. Introduction to sequence alignments. 
Durbin, et
al, chapter 2. On the number of possible sequence alignment (link). I plan to post additional lecture notes in HuskyCT. For students not familiar with algorithm design and analysis (especially dynamic programming), read these materials: Basic algorithm analysis Dynamic programming 
HW1.
Due: Sept. 12. End of day. Submit in HuskyCT. 