Justification & Course Overview

This is an introductory graduate course in Bioinformatics. The idea is to teach standard algorithmic and analysis techniques used in Bioinformatics. The ultimate goal is to train students to do research in Bioinformatics.

Prerequisite Knowledge

Data Structures & Algorithms (COP 3530 or equivalent), Discrete Math (MAD 2104 or equivalent), Probability & Statistics (STA 3033 or equivalent).

Topics

· Fundamentals of Biology, Statistics, and the Internet

· Overview of Bioinformatics, Computational Biology and Biotechnology

· (*) Databases and Software Packages, BioPerl and BioJava.

· (*) Sequence Alignment: problems and algorithms; Multiple Sequence Alignment

· (*) Phylogenetic Analysis

· (*) Sequencing and Mapping

· (*) Predictive Methods: Nucleotide Sequences and Protein Sequences

· (*) Pattern Discovery Techniques and applications

· (*) Machine Learning Methods: Neural Networks, Hidden Markov Models, Self-Organizing Maps, Support Vector Machines, etc.

· (*) Molecular Structural Analysis

· (*) Analysis of Gene Expression Data

· Advanced Topics -- Image Processing, Mathematical Modeling, Biocomputing.

The course will contain a lab component to learn Bioinformatics analysis tools. The tentative list of topics covered by the lab component are marked with a (*). Evaluation will include several homework assignments, exams and a semester project.

Texts and References

[Required]

· Developing Bioinformatics Computer Skills, Gibas & Jambeck, O’Reilly Publishers.

· Computational Molecular Biology, P. Pevzner, MIT Press.

[Reference]

· Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Eds. A. D. Baxevanis and B. F. Ouellette, Wiley Interscience, 2^nd ed., 2001.

· Algorithm on Strings, Trees, and Sequences, Gusfield, Cambridge Univ. Press, 1997.

· Biological Sequence Analysis, Durbin, Eddy, Krogh & Mitchison, Cambridge Press.

· Bioinformatics: The Machine Learning Approach, P. Baldi and S. Brunak. MIT Press.