Introduction to Computational Molecular Biology: Molecular Evolution

GENOME 541
Department of Genome Sciences
University of Washington
Spring Quarter, 2008

Course description:

This is the second quarter of a two-quarter introduction to protein and DNA sequence analysis and molecular evolution, including probabilistic models of sequences and of sequence evolution, computational gene identification, pairwise sequence comparison and alignment (algorithms and statistical issues), multiple sequence alignment and evolutionary tree construction, comparative genomics, and protein sequence/structure relationships. These are the central computational methods required to determine the "periodic table of biology," i.e., the list of proteins and their evolutionary relationships, which can be regarded as the first stage in the growth of molecular biology into a quantitative science. Moreover, the statistical and algorithmic methods used (which include maximum likelihood estimation, hidden Markov models and dynamic programming) have wide applicability in other areas of computational and mathematical biology.

Instructional staff

Instructor: Joe Felsenstein
Email:
Office: Foege S420B


Instructor: Larry Ruzzo
Email:
Office: Paul Allen Center 554


Instructor: David Baker
Email:
Office: Health Sciences J556


Instructor: William Stafford Noble
Email:
Office: Foege S220B


Instructor: Bruce Weir
Email: bsweir@u.washington.edu
Office: Health Sciences F665

Meeting times and locations

Tuesday and Thursday, 10:30 - 11:50 am, Foege Building S110.

Prerequisites

GENOME 540 or permission of instructor.

Students must be able to write computer programs for data analysis. Some prior exposure to probability, statistics and molecular biology is highly desirable.

Course materials

Required: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids by Richard Durbin, S. Eddy, A. Krogh, G. Mitchison; Cambridge University Press, 1998. ISBN: 0521629713. Paperback, ~$35.

Required: Statistical Methods in Bioinformatics : An Introduction (Statistics for Biology and Health) by Warren J. Ewens, Gregory R. Grant; Springer, 2005. ISBN: 0387400826. Hardbound, ~$90. Note that this is the (new) second edition of the text used in previous years. Make sure you get this edition.

Course requirements

• The entire course grade is based on the homework assignments, which are due weekly (more or less). No tests or exams.
• The homework assignments involve writing programs for data analysis, and running them on a computer that you have access to (we cannot provide computers). We don't require a specific language, since it is not practical to grade your code, just the output from running your programs.
• Homework is due by 11:59 pm on the indicated date. After that it will be accepted, but penalized. Specifically, each assignment is worth 100 points, from which 10 points will be deducted for each day (or fraction thereof) that you turn it in late. The maximum deduction for being late is 60 points (even if you are more than 6 days late). If you get less than 40 points on an assignment, you are allowed to redo it and take the new score (which will be 40, i.e. 100 - 60, if there are no mistakes).
• It is OK to run your program on someone else's input data file, and compare outputs to see if you get the same results. However it is not OK to share programs, or to get someone else to debug your program. A key part of the course is being able to write and debug your own programs for data analysis.

Examinations

None.

Course grade

12.5% for each homework assignment.

Home page

The course home page can be found at http://noble.gs.washington.edu/~noble/genome541 .

Class schedule

Date	Instructor	Topic	Reading	Homework
Tue Apr 1	Felsenstein	Trees, parsimony, compatibility	Ewens 497-499, 511-512, 517-521; Durbin 160-163, 173-176, 188-189
Thu Apr 3	Felsenstein	Counting trees, searching tree space	Ewens 511-512; Durbin 163-165, 176-179	HW1
Tue Apr 8	Felsenstein	Distances and distance matrix methods	Ewens 499-511; Durbin 165-173, 189-191
Thu Apr 10	Felsenstein	Models of DNA and protein change	Ewens 475-496; Durbin 193-197	HW2 (data)
Tue Apr 15	Felsenstein	Likelihood and Bayesian methods	Ewens 512-516, 409-416; Durbin. 197-210, 215-217
Thu Apr 17	Felsenstein	Testing and bootstraps	Ewens 295-300, 308-309, 313-318, 522-535; Durbin 179-180, 212-215	HW3
Tue Apr 22	Felsenstein	Coalescents	Durbin 211-212
Tue Apr 24	Felsenstein	Inference with coalescents	Ewens 392-398; Durbin 206-207, 211-212	HW4
Tue Apr 29	Ruzzo	Modeling and searching for non-coding RNA	Durbin, ch. 10
Thu May 1	Ruzzo	Modeling and searching for non-coding RNA		HW5
Tue May 6	Baker	Computational structural biology	Section on protein structure from any biochemistry textbook
Thu May 8	Baker	Computational structural biology
Tue May 13	Noble	Microarray analysis	(Pavlidis 2003) (Storey and Tibshirani 2003) (Brown et al. 2000) (Ramaswamy et al. 2001)
Thu May 15	Noble	Predicting protein function from heterogeneous data	(Lee et al. 2004) (Troyanskaya et al. 2003) (Lanckriet et al. 2004) (Noble 2006 or long version)	HW6
Tue May 20	Noble	Protein identification from tandem mass spectra	(Sadygov et al. 2004)
Thu May 22	Noble	Motif discovery		HW7
Tue May 27	Weir
Thu May 29	Weir			HW8
Tue Jun 3	Weir
Thu Jun 5	Weir