Handouts :   You can download the slides that I prepared for the lectures. Please keep in mind that I might not always follow the slides exactly. These slides are based on many other people's efforts, in particular Dr. T. Jiang.

		Events	Topics	Slides
Week 1	Sep09		Course information & Introduction	Lecture01.pdf
Week 2	Sep14, 16	Assignment 1 out	Biology background	Lecture02.pdf
			Line of research	Lecture03.pdf
Week 3	Sep21, 23		Course projects	Lecture04.pdf
			Open discussion on projects
Week 4	Sep28, 30	Project chosen	Sequence homolog search - demo	Lecture05.pdf
			Sequence homolog search - algorithms	Lecture06.pdf
Week 5	Oct05, 07		Sequence homolog search - improvements	Lecture07.ps
			Open discussion on sequence comparison
Week 6	Oct12, 14	Assignment 1 due Tue Quiz 1 out	Sequence/Structure comparison	Lecture08.pdf
			Structural comparison	Lecture09.pdf
Week 7	Oct19, 21	Assignment 2 out	Open discussion on structural comparison
			Gene finding	Lecture10.pdf
Week 8	Oct26, 28	Preliminary survey due	Open discussion on gene finding
			Genome rearrangement - models	Lecture11.pdf
Week 9	Nov02, 04		Genome rearrangement - algorithms	Lecture12.pdf
			Open discussion on genome rearrangement
Week 10	Nov09		Protein function prediction	Lecture13.pdf
Week 11	Nov16, 18	Assignment 2 due Tue Quiz 2	Protein function prediction	Lecture14.pdf
			Protein structure prediction	Lecture15.pdf
Week 12	Nov23, 25		Protein structure determination	Lecture16.pdf
			Open discussion on protein function/structure prediction/determination
Week 13	Nov30, 02		Phylogeny	Lecture17.pdf
			Phylogeny	Lecture18.pdf
Week 14	Dec07		Open discussion on phylogeny

Purpose :   The first half of the course is an introduction to the fields of Bioinformatics and Computational Biology. Instead of providing detail biological backgrounds, the course is based on certain given assumptions. It is concentrated on the concept of "what a computer scientist can do" in this brand new discipline at the intersection of biology, biochemistry, computer science, statistics, and mathematics. The second half is research oriented. Each student is encouraged/required to extensively survey one topic and perform a preliminary research, by applying what s/he has learned from all computer science courses and others.

Prerequisites :   Each student should possess the skills of algorithm design, analysis, and programming at the CMPUT 304 level or its equivalence. Knowledge of molecular biology and genetics are helpful but not really required (what s/he needs to do is to catch up some during the term). The enthusiasm is one of the most important key factors to achieve a high mark in this course, for otherwise s/he will find it too challenging to deal with.

Topics to be covered :

1. introduction and course projects (4 lectures);
2. homology
   • sequence similarity comparison (5 lectures),
   • structural similarity comparison (2 lectures),
   • homology search tools (1 lecture);
3. gene finding (2 lectures);
4. genome comparison
   • rearrangement (1+1 lectures),
   • other long range mutations, databases, and tools (1+1 lectures);
5. protein function/structure prediction/determination
   • how protein folds and prediction overview (1 lecture),
   • structure prediction/determination (2 lectures),
   • function prediction (2 lectures);
6. phylogeny
   • methods (2 lectures),
   • database and comparisons (1 lecture);
7. genomics and proteomics databases (2 lectures).
8. 20-minute course project presentations

References :   There is no textbook for this course. The following reference books have been reserved for reading in the (Cameron) Science and Technology Library.

1. T. Jiang, Y. Xu, and M. Zhang.
   Current Topics in Computational Molecular Biology. MIT Press. 2001.
2. D. Gusfield.
   Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge Univ. Press. 1997.
3. P. Pevzner.
   Computational Molecular Biology - an Algorithmic Approach.. MIT Press. 2000.
4. P. Baldi and S. Brunak.
   Bioinformatics: The Machine Learning Approach. MIT Press. 2001.
5. L. Gonick, and M. Wheelis.
   The cartoon guide to genetics. HarperInformation. 1991.
6. B. Lewin.
   Genes VII. Oxford Univ. Press. 1999.
7. H. Lodish et al.
   Molecular Cell Biology. W H Freeman & Co. 1999.
8. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein.
   Introduction to Algorithms (Second Edition). McGraw Hill. 2001.

Marking Scheme :

1. 2 (take-home) quizzes on the lecture contents. 10% each.
2. 2 assignments. 10% each.
3. There are 6 open discussion sessions on the topics covered. More references will be handed out for further reading. The participation worths 10% in total.
4. There will be a project for each student who takes this course for credit. Some projects will be programming oriented which might involve extensive simulation and comparative studies, some are research oriented which involves designing new algorithms. Every one of them could form the theme for a thesis.
   Preliminary survey. 10%.
5. The course project: 30%.
6. The final project report MUST be written in LaTeX, using the LNCS format. 10%
   Submit the following
   • .tex file, all special style files other than llncs.cls;
   • .ps or .pdf file;
   • all figures drawn using other tools than LaTeX;
   • the codes (C, C++, Java) and binaries, database(s), and documentation(s).