CSE 3800/5800 and BME 4800 (Fall 2017):  Bioinformatics,  Instructor: Yufeng Wu
Tentative Class Schedule (subject to change)

Week
Lectures
Homework
1
Introduction of bioinformatics: data and problems.
Part one: combinatorial algorithms for sequence alignment.
Sequence alignment: simple edit distance.
Pairwise sequence alignment: global and local and variations.
HW1. Basics of algorithm analysis and probability
2
Scoring matrices for sequence alignment.
The linear space alignment algorithm. The four-Russians trick.

3
Techniques for speeding up sequence alignment. Blast.
Multiple sequence alignment: exact, heuristic and approximation algorithms.
HW2: sequence alignment.
4
Part two: Hidden Markov Model
Probability and probabilistic model: distributions, entropy and sampling.
Introduction to statistical inference.

5
EM algorithm.
Introduction to hidden Markov model: an motivating problem. What types of problems can HMM solve?


6
Algorithms for HMM: forward, backward and Viterbi.
Parameter estimation: the Baum-Welch algorithm.
Programming assignment: implementation of HMM.
7
HMM models. Implementation issues with HMM.
HMM for pairwise sequence alignment.
HW3: probability and HMM.
8
Profile HMM.
HMM for multiple sequence alignment.
Project: proposal due
9
Part three: phylogenetics.
Introduction to phylogenetics. Counting of trees.
Parsimony. Perfect phylogeny.
Project: survey due.
10
Distance-based tree inference: UPGMA and neighbor joining.
Why does neighbor joining work?

11
Probability for phylogenetics.
Maximum likelihood inference of phylogeny.
HW4: phylogenetics
12
Part four: other topics
RNA folding.
Protein folding.
Project: status report due.
13
Genome rearragenment problems.
Algorithms for genome rearrangement problems.

14
Course project presentation.
Course project presentation.

15
Final exam.
Project: report due.