CAP 5937

Special Topics of Introduction to Bioinformatics

Instructor: Jianlin Cheng

Department: School of Electrical Engineering and Computer Science

Term: Fall Semester, 2006

Time: MW 3:00pm to 4:15pm

Office Hours: MW 4:15pm to 6pm

Location: ENG I 383

Prerequisite: Background in programming language OR molecular biology

Objective:

The course introduces fundamental problems, concepts, methods, and applications in Bioinformatics to students who are interested in this new interdisciplinary science. The course emphasizes both the methods and the practical use of bioinformatics tools and databases. A lot of exercises of using bioinformatics tools and mining biological databases are designed. Students are also required to apply the methods to solve a real, biological problem. The course consists of ten tentative topics and each topic has up to four lectures.

Topics:

1. Introduction to Molecular Biology and Bioinformatics

2. Pairwise Sequence Alignment Using Dynamic Programming

3. Practical Sequence/Profile Alignment Using Fast Heuristic Methods (BLAST and PSI-BLAST)

4. Multiple Sequence Alignment

5. Gene and Motif Identification

6. Phylogenetic Analysis

7. Protein Structure Analysis and Prediction

8. RNA Secondary Structure Prediction

9. Clustering and Classification of Gene Expression Data

10. Search and Mining of Biological Databases, Databanks, and Literature

Homework:

One assignment per week for the first half of the class, one project for the second half.

Exam:

Midterm and Final

Grading:

Homework: 20%, Project: 20%, Midterm: 30%, Final: 30%

Text:

No general text book is used.

Reference books:

1. Mount. Bioinformatics: Sequence and Genome Analysis. Cold Spring Harbor Laboratory Press, 2004. (Second Edition)

2. Baxevanis and Ouellette. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins (Third edition). John Wiley & Sons, 2004.

3. Baldi and Brunak. Bioinformatics: the Machine Learning Approach (Second edition). MIT press, 2001.

4. Pevsner. Bioinformatics and Functional Genomics. Wiley, 2003.

5. Jones and Pevzner. An Introduction to Bioinformatics Algorithms. MIT press, 2004.

6. Ewens and Grant. Statistical Methods in Bioinformatics: An Introduction (Second edition). Springer, 2006.

7. Durbin, Eddy, and Krogh. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1999.

8. Tramontano. Protein Structure Prediction: Concepts and Applications. John Wiley & Sons , 2006.

9. Xiong. Essential Bioinformatics. Cambridge University Press , 2006.

10. Wang, Zaki, Toivonen, and Shasha. Data Mining in Bioinformatics. Springer , 2004.