introduction to bioinformatics algorithms

Where: USC 310B

When: 10-11 MWF

course description

The past decade has seen an explosion of biological data, including the DNA sequences of a large number of organisms. Computer science and statistics play a major role in analyzing these data: from assembling the genomic sequence to predicting the protein-coding regions, their function and the manner in which they are regulated. The course will focus on biological sequence analysis, starting with the biological background motivating the various computational problems, and methods for their solution. Computational techniques covered in the course include dynamic programming for sequence alignment, hidden Markov models for gene finding, and Gibbs sampling for motif discovery.

The course is aimed at students interested in computational biology/bioinformatics. It is expected that students from computer science, statistics, and the life sciences will benefit from such a course. It will provide a broad overview of the computational and statistical techniques currently used in bioinformatics. Students will obtain in-depth understanding of standard bioinformatics tools and learn to use various sources of sequence data. The lab component of the course will consist of implementing and using methods for analysis of sequence data.

texts

• Neil C. Jones and Pavel A. Pevzner. An introduction to bioinformatics algorithms. MIT Press, 2004.
• Richard Durbin, Sean R. Eddy, Anders. Krogh and Graeme Mitchison. Biological Sequence Analysis. Cambridge UP, 1998.

prerequisites

(a) ST301 or ST307 or ST309, and (b) CS301 or ST420, OR permission of instructor