CMPUT 606: Bioinformatics (Fall 2015)

An introduction to the current research in Bioinformatics and Computational Biology allowing you to get some hands-on experience by working on a course project, where various computing techniques might be applied.

Course Objectives:

  • Learn some current research hot 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)
A1 (CSC B41)
A1 (CSC B41)

Guohui Lin (instructor)
ATH 353
guohui TA ualberta TOD ca

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):

  • 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.
  • L. Gonick and M. Wheelis (1991). "The Cartoon Guide to Genetics". Harper Perennial.
  • D. Gusfield (1997). "Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology". Cambridge Univ. Press.
  • A. M. Lesk (2002). "Introduction to Bioinformatics". Oxford Univ. Press.

Lecture Schedule:

Week Date Lecture Topics
1 Sep 1, 3 Course overview; biology background introduction
2 Sep 8, 10 Sequencing
3 Sep 15, 17 Sequence comparison
4 Sep 22, 24
Invited talk by Yining Wang on the 22nd (research travel);
Missing data imputation
5 Sep 29, Oct 1 Genotyping and genome-wide association study
6 Oct 6, 8 Student paper presentation (15% each)
  1. (K.N.)Horizontal gene transfer: building the web of life; Nature Reviews Genetics 16, 472-482 (2015)
  2. (A.J.)Machine learning applications in genetics and genomics; Nature Reviews Genetics 16, 321-332 (2015)
  3. (B.B.)Genomes by design; Nature Reviews Genetics 16, 501-516 (2015)
7 Oct 13, 15 Gene expression and data analysis
8 Oct 20, 22 Phylogenetic analysis
9 Oct 27, 29 Protein properties
10 Nov 3, 5 Student paper presentation (15% each)
  1. (A.J.)Content discovery and retrieval services at the European Nucleotide Archive; Nucleic Acids Research 43, Issue D1, Pp. D23-D29 (2015)
  2. (B.B.)Detecting epistasis in human complex traits; Nature Reviews Genetics 15, 722-733 (2014)
  3. (K.N.)RNAcentral: an international database of ncRNA sequences; Nucleic Acids Research 43, Issue D1, Pp. D123-D129 (2015)
11 Nov 10, 12 Reading week
12 Nov 17, 19 Protein structure determination and prediction
13 Nov 24, 26
Course project survey (40%) due at the beginning of class on the 24th
14 Dec 1, 3
A.J.'s course project presentation

K.N.'s course project presentation
B.B.'s course project presentation

Course project final report (30%) due by the end of Dec 7

Grading Scheme:

  1. General scheme:
    There is no homework and no exam. After each topic, a research seminar of 10-20 minutes is designed to discuss possible research project. Students are encouraged to bring their research project to the research seminar. Each student takes on a project to do a survey, design new algorithms, perform new experiments, and write a final report. Grade will be given based on the research performance of each student.
  2. Mark distribution:
    • 30%     2 paper presentations (15% each)
    • 40%     1 course project survey
    • 30%     1 course project final report (survey part excluded; presentation helps marking)
  3. Notes:
    • No late hand-ins will be accepted; print a hard-copy of your work for your instructor.

  Last modified: November 21 2015 12:49:50  © Guohui Lin