Course: Computational Genomics and Bioinformatics

Course Code: CSB 1472H/S, Teaching Section LEC 0101


Professor David Guttman

Offered Winter 2023 session

Enrolment limited to 8 graduate students (CSB grads with priority)


Half credit (0.5 FCE)


Wednesdays 10 am – 12 pm


St. George campus, UC 87

*CSB1472H/S is a half-credit course that takes place during the full Winter session.  It is the equivalent of two modules.  Graduate students should NOT request the course using the undergraduate course code CSB472H1S, because it would not count toward graduate credit. 

Course Description

Recent technological advances have driven a revolution in genomics research that has had a direct impact on both fundamental research as well as direct application in nearly biological disciplines. These advances have made the generation of genomic data relatively straightforward and inexpensive; nevertheless, the data are meaningless if they cannot be properly analyzed.  Computational genomics and bioinformatics are the tools we use to extract biological information from complex genomic data.

CSB1472 will teach you the fundamentals of analyzing genomic data.  This course emphasizes understanding how core bioinformatic analyses work, the strengths and weaknesses of related methods, and the important parameters embedded in these analyses.  CSB1472 is not an applied methods course, nor a course to for developing new bioinformatic tools, but rather a course designed to provide you with a basic understanding of the principles underlying genome analyses.  We will examine the fundamentals of sequence alignment, phylogenetic analyses, genome annotation, gene prediction, and gene expression data analysis.  Theoretical, applied, and statistical issues will be addressed.

The material is presented as an inverted course.  Lectures are pre-recorded and available prior to class. Class time is devoted to review of the lecture material, discussion of the primary literature related to the course material, and hands-on analysis laboratories.

Recommended Text

Zvelebil & Baum 2008 Understanding Bioinformatics. Garland Science, New York.

Last updated on August 11th, 2022