The course is a 3-credit graduate course which provides students with examples of bioinformatic problems and, in a team environment; the students develop strategies and identify resources which allow them to determine solutions. This problem-based learning (PBL) course develops a students’ ability to exchange ideas in small groups focused on real but simplified problems in bioinformatics. Problems are carefully selected to cover all aspects of bioinformatics research.

Course Details

This is a 3-credit discussion-based graduate course that acquaints the students with the latest developments in bioinformatics analysis and algorithms. It runs in conjunction with the Vancouver Bioinformatics User Group (VanBUG) Seminar Series (http://vanbug.org), in which the students will have an opportunity to meet and discuss their work with both local and international guest speakers. During this course, students will prepare presentations (individually and in groups) on recent papers in bioinformatics, genomics and proteomics.

Course Details

This is an introductory level graduate course on fundamental computational techniques which have been successfully applied to key problems in bioinformatics. Particular problem areas of interest include sequence alignment and search, motif discovery, molecular structure prediction, phylogenetics, biomolecular interactions and cellular networks. We will cover various computational tools ranging from ones which are combinatorial in nature, such as dynamic programming, index structures, approximation algorithms, and randomized algorithms to those which are statistical such as expectation maximization and Gibbs sampling.

Course Details

This is an introductory level graduate course on fundamental computational techniques which have been successfully applied to key problems in bioinformatics. Particular problem areas of interest include sequence alignment and search, motif discovery, molecular structure prediction, phylogenetics, biomolecular interactions and cellular networks. We will cover various computational tools ranging from ones which are combinatorial in nature, such as dynamic programming, index structures, approximation algorithms, and randomized algorithms to those which are statistical such as expectation maximization and Gibbs sampling

Course Details

This course will cover quantitative problems arising from current research. We focus on areas in which a statistical approach provides a powerful tool for separating signal from noise. Students will learn to translate genomic research questions into well-defined computational problems. Solutions and algorithms are found which are both theoretically sound and practical to implement. Selected topics: gene expression analysis, analysis of tissue and protein arrays, sequence alignment and comparison, Hidden Markov Models.

Course Details

If you have already taken any of these courses as an undergraduate or have taken equivalent material at another University, you are not required to repeat the material, rather choose an additional elective to make up the course requirements for your degree program.

Electives – Please browse the UBC and SFU courses to complete your other elective requirements.