Course Description
Technological advances have driven a genomics revolution that is transforming nearly every area of biology. These innovations have vastly expanded the scale, complexity, and speed at which genomic data can be generated. However, while producing genomic data has become faster and more affordable, analyzing it effectively remains a significant challenge. Computational genomics and bioinformatics provide the methods and tools needed to interpret these complex datasets and extract meaningful biological insights.
CSB472 introduces the foundational principles of biological data analysis, with a strong emphasis on understanding how core bioinformatic algorithms work. Rather than focusing on tool usage or software development, this course explores the underlying concepts, strengths and limitations of key analytical methods, and the critical parameters that influence results. Topics include sequence alignment, database searching, phylogenetic reconstruction, genome annotation, gene prediction, and the analysis of gene expression data.
This is not an applied bioinformatics course or a course in tool development—it is designed for students who want to understand the theory and logic behind genome-scale analyses. If your primary interest lies in learning how to use bioinformatics tools rather than how they work, you may wish to consider CSB352, which is more application-focused.
Prerequisite
BIO230H1 / BIO255H1
Recommended Preparation
BIO260H1 / HMB265H1
Lecturer(s)
Prof. D. Guttman
Prof. N. Provart
Contact Hours
24L, 12T
Recommended text(s)/Readings
Jonathan Pevsner, Bioinformatics and Functional Genomics, 3rd edition (2015)
Evaluation (Subject to change)
One assignment/presentation: 15%
Midterm: 30%
Final: 30%
Quizzes: 15%
Participation: 10%
Last updated on July 2nd, 2025