||Course project presentation
||Lecture 26: RNA.
Lecture 25: Transformational grammars.
|Chapters 9 and 10.
||Lecture 24: Neighbor joining. Parsimony.
Lecture 23: Distance methods for inferring phylogeny.
||HW5. Due: 11/30.
||Lecture 22: Introduction to phylogenetics.
Lecture 21: Profile HMM. Multiple sequence alignment with HMM.
||HW4. Due: 11/16.
||Lecture 20: Profile HMM.
Weighting of sequences.
Lecture 19: Profile HMM. Pseudo counts.
||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)
||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.
||Lecture 14: Parameter estimation using
Baum-Welch algorithm. EM algorithm. Model structure.
Lecture 13: HMM: forward/backward algorithms. Posterior decoding.
assignment. Due: 11/3.
Note: deadline extended.
||Lecture 12: HMM. Viterbi
Lecture 11: Markov model. Hidden Markov Model.
||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.
Due: 10/10. Note this is a Tuesday.
||Lecture 8: Multiple sequence
Lecture 7: Extreme value distribution and sequence alignment. Multiple sequence alignment.
|Section 11.1. Lecture notes
by Gusfield (see HuskyCT).
||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.
Due: Sept. 28, end of day. Submit in HuskyCT.
||Lecture 4: Alignment in
Variations of alignments
Lecture 3: Sequence alignment: traceback, local alignment.
|Chapter 2 and lecture notes.
Gusfield's notes on linear space alignment.
Sequence alignment with dynamic programming.
Lecture 1: Introduction of bioinformatics.
Introduction to sequence alignments.
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
Due: Sept. 12. End of day. Submit in HuskyCT.