CMPUT 606: Optimization in Bioinformatics (Winter 2023)

An introduction to the classic and current research in Bioinformatics and Computational Biology allowing you to get some hands-on experience by working on a course project. We will introduce various formulated optimization problems and you will find various computing techniques applicable.

Course Objectives:

  • Learn some classic and currently hot research topics in bioinformatics and computational biology;
  • learn computing techniques for this area of research;
  • be familiar enough to explain these topics to others and understand what contributions you could make.

Code of Student Behavior

Department Course Policies

Office hour (ATH 353)
Possible project meetings
Possible project meetings
Lecture B2 (HC 234)

Recording of teaching is permitted only with the prior written consent of the instructor or if recording is part of an approved accommodation plan.

Recommended readings (no required textbook):

  • L. Gonick and M. Wheelis (1991). "The Cartoon Guide to Genetics". Harper Perennial.
  • A. M. Lesk (2002). "Introduction to Bioinformatics". Oxford Univ. Press.
  • D. Gusfield (1997). "Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology". Cambridge Univ. Press.
  • P. Baldi and S. Brunak (2001). "Bioinformatics, the Machine Learning Approach". The MIT Press.
  • T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein (2009). "Introduction to Algorithms (Third Edition)". The MIT Press.

Lecture Schedule:

Week Date Lecture Topics
1 Jan 6 Course overview;
discussion on how to run the course (below is one version);
biology background introduction
2 Jan 13 Sequencing
3 Jan 20 Sequence and string comparison
4 Jan 27 Phylogenetic analysis
Exercise #1 (10%) on sequencing due 26th
5 Feb 3 Genotyping and genome-wide association study
Exercise #2 (10%) on sequence comparison due 2nd
6 Feb 10 Gene expression and data analysis
Exercise #3 (10%) on phylogenetic analysis due 9th
7 Feb 17 Missing data imputation
Exercise #4 (10%) on GWAS due 16th
8 Feb 24 Family Day, Reading Week
9 Mar 3 Student project proposal presentations
  1. 2:10pm (see Google Drive)
  2. 2:40pm
  3. 3:10pm
  4. 3:40pm
  5. 4:10pm
Exercise #5 (10%) on clustering, classification, regression due 2nd
10 Mar 10 Student project proposal presentations
  1. (see Google Drive)
Exercise #6 (10%) on imputation due 9th
11 Mar 17 Protein properties
12 Mar 24 Protein structure comparison, determination, and prediction
13 Mar 31 Proteomics and metabolomics
14 Apr 7 Good Friday
Course project final report due 12th

Grading Scheme:

  1. Check my marks
  2. General scheme:
    There is no exam.
  3. Mark distribution, two possibilities:
    • 60%  6 small programming exercises or numerical experiments (10% each);
    • 20%  course project proposal presentation (project, existing work, methods, expected results, rationale);
    • 20%  course project final report (literature review excluded).
    Or (Lectures 11, 12 could be pulled earlier to allow for presentations)
    • 70%  course project proposal development and presentation;
    • 30%  course project final report (literature review excluded).
  4. Notes:
    • Each student to create a GoogleDrive (w/sub-directories) containing all your work to share with your instructor.


Disclaimer: Any typographical errors in this Course Outline are subject to change and will be announced in class.


  Last modified: April 04 2023 08:56:06  © Guohui Lin