Biostatistics & Medical Informatics 576
Computer Sciences 576
Introduction to Bioinformatics (Fall 2014)

General Course Information
Course overview
Syllabus, Readings, Lecture Notes
Schedule
Homework Assignments
Grading criteria
DNA

Tentative Course Schedule

Week Date Section Lecture topics
1 9/2 Introduction Class Overview
9/4 DNA sequence Introduction to Sequence alignment
2 9/9 Pairwise sequence alignment
9/11 Pairwise sequence alignment
3 9/16 Multiple sequence alignment
9/18 Multiple sequence alignment
4 9/23 Phylogenetic Trees Intro to Phylogenetics
9/25 Distance-based methods (Neighbor Joining algorithm)
5 9/30 Parsimony methods
10/2 Probabilistic methods
6 10/7 Phylogenetics in practice
10/9 Genome annotation Introduction to sequence annotation/Markov models
7 10/14 Mid-term Mid-term
10/16 Hidden Markov models: forward/backward/viterbi algorithm
8 10/21 Hidden Markov models: Parameter estimation
10/23 Parameter estimation
9 10/28 Applications of Markov models (ChromHMM)
10/30 Clustering of omic datasets Introduction to "omic" datasets
10 11/4 Flat clustering
11/6 Gaussian mixture model-based clustering
11 11/11 Hierarchical clustering
11/13 Cluster evaluation and Clustering in practice
12 11/18 Modeling and analysis of networks Introduction to biological networks
11/20 Bayesian networks
13 11/25 Bayesian networks for gene expression data
11/27 Thanksgiving. No class.
14 12/2 Module networks
12/4 Dependency networks
15 12/9 Application of networks
12/11 Class review Class review
16 12/16 Final Exam Final Exam