Introduction to Computational Molecular BiologyGENOME 541
Department of Genome Sciences
University of Washington
Spring Quarter, 2014
This is the second quarter of a two-quarter introduction to computational methods for analyzing biological data. The course surveys a variety of subfields in computational biology, including various types of sequence analyses—phylogenetic footprinting, searching for non-coding RNAs, motif discovery—as well as microarray analysis, proteomics, systems biology, computational cell biology and computational structural biology.
Please click on the links above for email addresses and office locations.
Meeting times and locations
Tuesday and Thursday, 10:30 - 11:50 am, Foege Building S110.
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.
No textbook is required for this class.
- The entire course grade is based on the homework assignments, which are due weekly. 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.
- Late homework 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).
- 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.
- Homework assignments should be turned in using the Catalyst Tools Dropbox.
10% for each homework assignment.
The course home page can be found at http://noble.gs.washington.edu/~noble/genome541 .
Date Instructor Topic References Homework
Tue Apr 1 Noble Predicting protein function from heterogeneous data (Noble 2006) Thu Apr 3 Noble Predicting protein function from heterogeneous data (Brown et al. 2000) (Ramaswamy et al. 2001) (Lanckriet et al. 2004) Homework #1 Tue Apr 8 Borenstein Complex biological networks Thu Apr 10 Borenstein Complex biological networks Homework #2 Tue Apr 15 Ruzzo Non-coding RNA Thu Apr 17 Ruzzo Non-coding RNA Homework #3 Tue Apr 22 Noble Motif discovery (D'Haeseleer 2006) (Noble 2009) Thu Apr 24 Noble Protein identification from tandem mass spectra (Sadygov et al. 2004) (Marcotte 2007) Homework #4 Tue Apr 29 Bloom Molecular evolution (Zuckerkand and Pauling) Thu May 1 Bloom Molecular evolution Homework #5 Tue May 6 Bloom Phylogenetic inference Homework #6 Thu May 8 Bloom Phylogenetic inference Tue May 13 Bradley Protein structure Thu May 15 Bradley Protein sequence analysis Homework #7 Tue May 20 Bradley Molecular modeling Thu May 22 Bradley Nucleic acid structure Homework #8 Tue May 27 Qian Stochastic models of single cells and single molecules (Qian 2012) (Qian and Kou 2014) Thu May 29 Qian Stochastic models of single cells and single molecules Homework #9 Tue Jun 1 Lee Statistical methods for inferring gene regulatory networks Thu Jun 3 Lee Statistical methods for inferring gene regulatory networks Homework #10