Course Description
Current approaches to using the computer for analyzing and modeling biology as integrated molecular systems. Lectures plus hands-on practical exercises. The course extends and complements an introductory Bioinformatics course, such as MGY441H1. In this course, you will get to explore gene expression analysis. Everyone will select their own dataset from GEO. Once you have selected your dataset you will:
- process and clean the dataset and examine global properties of the data. (Assignment #1)
- conduct differential gene expression analysis to uncover the strongest signals in the data. (Assignment #2)
- perform pathway and network analysis to uncover novel aspects about your dataset. Incorporate and integrate additional data sources to complement your dataset analysis. (Assignment #3)
Prerequisite
BCH441H1/MGY441H1/ BCB410H1 and good working knowledge of R
Lecturer(s)
Prof. R. Isserlin
Contact Hours
24L
Required Text(s)/Readings
All course material will be available via Quercus
Evaluation (Subject to change)
Initial set up: 5%
Quiz on preparatory material: 20%
Assignment 1: 20%
Assignment 2: 20%
Assignment 3: 20%
Journal/Insights: 15%
Additional Information
The course requires:
- a solid understanding of molecular biology,
- a solid, introductory level knowledge of bioinformatics,
- a good working knowledge of the R programming language.
Bring your own laptop to class for hands-on practice and software implementation of the methods learned.
Last updated on June 7th, 2024