Computational Methods in Bioinformatics (Ref.#18051) Fall 2005

Instructor : Dr. Dong Xu or Trupti Joshi
Office Hours: 4:45pm - 5:35 pm, Tuesday and Thursday (Room 271C Life Science Center; Room 110 Life Science Center)

Class Meeting Time : 3:30pm – 4:45pm, Tuesday and Thursday

Class Meeting Place : Room 272 Life Science Center


Prerequisites

• CS 2050 (Algorithm Design and Programming II) or equivalent training
• STAT 2500 (Introduction to Probability and Statistics I) or equivalent training
• Programming skills in a major programming language are required
• No biology background is necessary

Course Objectives

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, design of scoring function and algorithm, confidence assessment of prediction result, and software development.

Topics

• biological sequence comparison
• phylogenetic tree
• protein structure comparison
• protein structure prediction
• RNA secondary structure prediction
• gene finding
• gene expression data analysis
• DNA regulatory binding motif search
• proteomics data analysis
• bioinformatics tool development


Recommended References

• Neil C.Jones and Pavel A. Pevzner: An Introduction to Bioinformatics Algorithms (Computational Molecular Biology). MIT Press, 2004.
• 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.
• Terry Speed: Statistical analysis of gene expression of gene expression microarray data. Chapman&Hall/CRC. 2003.

Resource Link


Grading Scheme

The grades are based on the exams and course project. 2 take-home exams (20% each) and Project : 3 Phase Reports (5% each), Final Report (15%), Software Demo (15%), Presentation (15%)

Academic Honesty

Academic honesty is fundamental to the activities and principles of a university. All members of the academic community must be confident that each person's work has been responsibly and honorably acquired, developed, and presented. Any effort to gain an advantage not given to all students is dishonest whether or not the effort is successful. The academic community regards academic dishonesty as an extremely serious matter, with serious consequences that range from probation to expulsion. When in doubt about plagiarism, paraphrasing, quoting, or collaboration, consult the course instructor.

Special Needs

If you have special needs as addressed by the Americans with Disability Act (ADA) and need assistance, please notify the Office of Disability Services, A048 Brady Commons, 882-4696 or course instructor immediately. Reasonable effort will be made to accommodate your special needs.