This course provides an introduction to current and emerging methods used to generate genomic data analyzed in bioinformatics. This may include techniques for DNA sequencing as well as transcriptome, proteome and metabolome analysis. The objective is to develop an appreciation for the challenges of producing data.

This course familiarizes students with tools for the computational acquisition and analysis of molecular biological data. Key software for biological data acquisition, management, analysis, and visualization are presented. Laboratory exercises guide students through application of relevant tools.

This course introduces students to computer programming in languages relevant for contemporary bioinformatics. Students apply these programming skills to perform bioinformatics data analyses.

This course is an overview course on different approaches to analyze biological sequences. Basic concepts are introduced, as well as related algorithms.

Background literature pertinent to the student's initial research direction is studied. Starting with a reading list provided by the advisor and the instructor, the student builds on this list and constructs a major literature review over two semesters. As the student begins to generate initial ideas for their own research direction, their ideas for their doctoral research are written and explained. The emphasis is on a sub-field or sub-fields of bioinformatics.

The course covers a breadth of knowledge of topics in bioinformatics, which may include, but are not limited to, programming languages and development, computing skills applicable to artificial intelligence and machine learning strategies, and multi-OMICs software packages and their applications in diverse biological fields. Additionally, critical thinking, communication, presentation, and collaboration skills are developed and fostered.

This course presents a selection of advanced approaches for the statistical analysis of data that arise in bioinformatics, especially genomic data. A central theme to this course is the modelling of complex, often high-dimensional, data structures.

A major research project and paper is completed and presented by students in the Master of Bioinformatics program. Projects may involve either the development or application of bioinformatics methods. Professionalism and communication skills in written, oral, visual, and computational formats are also emphasized.