CSC2417: Algorithms for Genome Sequence Analysis

Winter 2015

Lectures: F 10-12 in Bahen 2165

Instructor: Michael Brudno
Office: Pratt (PT) 286C
Office Hours: by appointment


General information

This graduate course will cover some exciting algorithms that have been developed to analyze genomic and functional data, including Genome comparison and assembly, gene prediction, localization of regulatory elements in the genome, and analysis of epigenetic data. While the emphasis of the class will be on discrete algorithms, we occasionally will talk about probabilistic models (such as HMMs), and the interplay between discrete and probabilistic models. The course is intended for Computer Science graduate students, and all of the required biology will be explained in the class. Students in biological and related sciences with a strong computational background are encouraged to participate.

Expected Background:
Students should be familiar with algorithms (at least CSC 373 level), basic probability theory.

The basic requirements for the class will be a course project (45% of the grade), three homework assignments (45% of the grade), and class participation (10%).

The project can be on any topic (loosely) related to computational biology.

Administrative details:
For CS MSc students this class is in breadth category 4 (Human Centric and Interdisciplinary computing).