CSC2417: Algorithms for Genome Sequence Analysis
Lectures: F 10-12 in Bahen 2165
Instructor: Michael Brudno
Office: Pratt (PT) 286C
Office Hours: by appointment
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.
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
For CS MSc students this class is in breadth category 4 (Human Centric and Interdisciplinary computing).