CS7010 Computational Methods in Bioinformatics
Overview:
This 3 credit hour course introduces the fundamental concepts and basic computational techniques for several mainstream bioinformatics problems. Emphasis will be placed on the computational aspect of bioinformatics, including problem formulation from biological problem into a computable problem, scoring function development, algorithm design, confidence assessment of prediction result, and software implementation.
Textbook: No textbook. The following 5 reference books are used:
• Pavel Pevzner: Computational Molecular Biology - An Algorithmic Approach. MIT Press, 2000.
• Current Topics in Computational Molecular Biology, edited by Tao Jiang, Ying Xu, and Michael Zhang. MIT Press. 2002.
• Pierre Baldi and Soren Brunak: Bioinformatics – The Machine Learning Approach (second edition). MIT Press, 2001.
• Dan Gusfield: Algorithms on Strings, Trees, and Sequences. Cambridge University Press. 1997.
• Warren J. Ewens and Gregory R. Grant: Statistical Methods in Bioinformatics – An Introduction. Springer. 2001.
Prerequisites:
• CS 203 (Algorithm Design and Programming II) or equivalent training
• Statistics 150 (Introduction to Probability and Statistics I) or equivalent training
• Programming skills in any programming language
• No biology background is necessary
Topics: 1. course introduction (1 lecture)
2. computational problems in molecular biology (1 lecture)
3. gene finding (2 lectures)
4. bioinformatic tool development / project assignment (1 lecture)
5. gene expression data analysis (5 lectures)
6. DNA regulatory binding motif search (2 lectures)
7. proteomics data analysis (1 lecture)
8. pair-wise sequence comparison (3 lecture)
9. multiple sequence comparison (2 lectures)
10. phylogenetic tree (2 lectures)
11. protein secondary structure prediction (2 lectures)
12. protein tertiary structure comparison (2 lectures)
13. protein tertiary structure prediction (3 lectures)
14. Final project presentation (2 lectures)
Prepared by: Dong Xu
Date: September 2004
|
