Course Description

Practical introduction to concepts, standards and tools for the implementation of strategies in bioinformatics and computational biology. Student led discussions plus a strong component of hands-on exercises. The “Applied Bioinformatics” course is offered as a part of the Bioinformatics and Computational Biology Program curriculum to ensure that our students know enough about application issues in the field to be able to put their knowledge into practice in a research lab setting. This is to support the Specialist Program goal: to prepare students for graduate studies in the discipline. As a required course in the BCB curriculum, BCB410 assumes the prerequisites and goals of fourth-year students in the BCB Specialist Program. Other students may be permitted to enroll on a case-by-case basis, space permitting, but they may need to catch up on prerequisites in computer science or life-science courses that BCB students have taken at this point. Generally speaking, this is an advanced course that presupposes familiarity with programming principles, algorithm analysis, and methods of modern systems biology, as well as introductory knowledge of linear algebra, graph theory, information theory, statistics, as well as molecular-, structural- and cellular biology. The varying topics will be discussed at a highly technical level that is likely only useful for students who plan to integrate much of this material into their actual practice.

The course will consist of five phases:

Section I:
Cover topics on R, reproducibility, ethics, structure of an R package, and explore R packages.

Section II:
Define a tool for biological data analysis.

Section III:
Develop the tool into an R package following best practices.

Section IV:
Initial submission and review of peer R packages.

Section V:
Improve own package, Shiny application development and final submission.

Prerequisite

BCH311H1 / CSB349H1 / MGY311Y1,
(CSC324H1 / CSC373H1 / CSC375H1) or permission of the course coordinator

Lecturer(s)

Anjali Silva. PhD

Contact Hours

24L

Required Text(s)/Readings

All course material will be available via Quercus and GitHub

Recommended Text(s)/Readings

– R packages (2e) by Hadley Wickham and Jennifer Bryan; Access https://r-pkgs.org/index.html

– Advanced R by H. Wickham. Access: https://adv-r.hadley.nz/index.html

– R for Data Science by H. Wickham. Access: https://r4ds.had.co.nz/

– Happy Git and GitHub for the use R by J. Bryan. Access: https://happygitwithr.com/index.html

– For more resources, see lecture notes.

Evaluation (Subject to change)

Assessment 1 – Academic Integrity: 10%

Assessment 2 – R packages: CRAN, Bioconductor, GitHub: 10%

Assessment 3 – Outline of R package: 10%

Assessment 4 – Initial Submission and Review Sessions: 30%

Assessment 5 – Final Submission of R package– 40%

Additional Information

In this course you will define a useful tool for the analysis of biological data, write an R package to support it following best practices, review and critique other packages, improve and document your work, and submit the finalized tool with an implementation of a Shiny application to support the R package. You must have access to Internet and a computer with administrative privileges for hands-on practice and software implementation. For topics, and deadlines, see syllabus.

Last updated on June 21st, 2024