BME 4800 and CSE 3800/5800:  Bioinformatics
Fall 2017

Instructor: Yufeng Wu

Lecture: Tuesday and Thursday 3:30-4:45 pm, ITE 119.

Office Hour: Tuesday and Thursday 9:30-11:30 am, or by appointment. Location: ITE 235.


Course Description
. See the Syllabus.

Latex. I will appreciate if you can typeset your homework solutions. Also, you are required to typeset the project report. Latex is a nice tool to learn. If you have no experience with Latex, you may want to start here.

Planned schedule is here, but this is what is happening:

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.
Chapter 7.
HW5. Due: 11/30.
Lecture 22: Introduction to phylogenetics.

Lecture 21: Profile HMM. Multiple sequence alignment with HMM.
Chapter 6.
HW4. Due: 11/16.
Lecture 20: Profile HMM. Weighting of sequences.

Lecture 19: Profile HMM. Pseudo counts.
Chapter 5.

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.
Chapter 3.
Programming assignment. Due: 11/3. Note: deadline extended.
Lecture 12: HMM. Viterbi algorithm.

Lecture 11: Markov model. Hidden Markov Model.
Chapter 3.

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.
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).

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.
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.

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.