Probabilistic Models in Computational Genomics

COM SCI C221 - Probabilistic Models in Computational Genomics

Introduction to Bioinformatics

CHEM CM160A - Introduction to Bioinformatics

<p>(Formerly numbered CM121.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 32 or Program in Computing 10C with grade of C– or better, and one course from Civil and Environmental Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Prior knowledge of biology is not required. Designed for engineering students as well as students from biological sciences and medical school. Introduction to probabilistic models in the context of genomics, with emphasis on concepts and inventing new computational and statistical techniques to analyze genomic data. Concurrently scheduled with course C221. Letter grading.</p>

Computer Science

Henry Samueli School of Engineering and Applied Science

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COM SCI C121 - Probabilistic Models in Computational Genomics

Students will learn a graph algorithm to assemble genome, a dynamic programming algorithm to perform sequence alignment, and pattern matching algorithms such as suffix tree and Burrows-Wheeler Transform.

The goal of the class is not only to learn the concepts but also to apply the algorithms to solve real biology problems.

Students will also learn important statistical concepts such as hypothesis testing, multiple testing correction, linear regression, and a hidden Markov model.

General Catalog

Probabilistic Models in Computational Genomics

COM SCI C121

4 units