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

 

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.

 

Exam:

 

Midterm and Final

 

Grading:

 

Project and Homework: 40%, Midterm: 30%, Final: 30%

 

Text:

 

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

 

 

Reference books:

 

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

    

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

 

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

 

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

 

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