There is no required textbook for this course. But this following book might give you an easy start.

Biological sequence analysis: probabilistic models of proteins and nucleic acids by Richard Durbin, Sean R. Eddy, Anders Krogh, and Graeme Mitchison

This course focuses on commonly used probabilistic models and algorithms in computational biology. Both canonical methods and their potential applications to new problems in computational biology will be covered. Fox example, we will have in-depth covering of various sequence alignment algorithms and also explore how to adapt them for short-sequence mapping problem, which is often needed to analyze high-throughput sequencing data sets.

Topics covered include: 1. dynamic programming algorithm and its applications in pairwise sequence alignment, spliced alignment, MSA, sequence mapping problem etc. 2. Combinatorial pattern matching, including exact pattern match, approximate pattern match, suffix tree, hashing etc. 3. Randomized algorithm and its applications in motif finding. 4. EM algorithm 5. Grammar and ncRNA identification. 6. Hidden Markov model and its application in sequence alignment, gene prediction, and protein homology search.

This course can help you achieve following goals: (1) understand active research problems in computational biology; (2) become more efficient when reading research papers in computational biology; (3) obtain in-depth understanding of important computational methods and probabilistic models in computational biology or related fields. In particular, for students with biology backgrounds, this course will provide you an opportunity to look into the "black box" of many popular bioinformatics tools.

Prior programming experience is expected for this course. Any programming language is fine, including C/C++, Matlab, Java, Python, perl, etc.

This course contains about five homework assignments and a final project. Each homework assignment requires students to solve a well-defined computational biology problem using real biological data. In addition, students are expected to write critics for related research papers. The final project will be a group project; collaborations between students with different backgrounds are encouraged.

Grading: homework 65%, final project 35%.

The final grades will be assigned based on the following scale:
≥ 90% 85% 80% 75% 70% 65% 60%
4.0 3.5 3.0 2.5 2.0 1.5 1.0
The instructors reserve the right to make changes that are needed for proper delivery of the course.
In particular, the grading scale cut points may be moved downward but not upward (say 85%
becomes 83% in the end)