Introduction to Computational Molecular BiologyGENOME 541
Department of Genome Sciences
University of Washington
This course provides a survey of topics within the field of computational molecular biology. The course is divided into five two-week blocks, each devoted to a single topic and taught by a different instructor. This year, the topics include
- proteins and proteomics,
- protein structure,
- genomics and epigenomics, and
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 Topic References Homework
Proteins and proteomics—Bill Noble Tue Mar 31 Protein remote homology detection HHPred, SVM primer, Fisher SVM, RankProp, Protembed Thu Apr 2 Predicting protein function from heterogeneous data Bayesian networks, multiple kernel SVMs, GeneMania Homework #1 Tue Apr 7 Analysis of tandem mass spectra - I Review, SEQUEST, X!Tandem, Target-decoy search, MS-GF+ Thu Apr 9 Analysis of tandem mass spectra - II Percolator, IDPicker, EVD, Barista Homework #2 Protein structure—Frank DiMaio Tue Apr 14 Protein structure Thu Apr 16 Molecular modeling—Macromolecular forcefields Homework #3 Tue Apr 21 Molecular modeling—optimization methods Thu Apr 23 Nucleic acid structure Homework #4 Metagenomics—Ohad Manor Tue Apr 28 Metagenomics Thu Apr 30 Deconvolution and linear regression Homework #5 Tue May 5 Pathway analysis in metagenomics Thu May 7 Predicting disease state with k-nearest-neighbour and random forest Homework #6 Gene regulation and epigenomics—Max Libbrecht Tue May 12 Transcription factor binding Thu May 14 Transcription factor binding Homework #7 Tue May 19 Functional element discovery Thu May 21 Chromatin 3D modeling Homework #8 Phylogenetics—Jesse Bloom Tue May 26 Molecular evolution (Zuckerkand and Pauling) Thu May 28 Molecular evolution Homework #9 Tue Jun 2 Phylogenetic inference Homework #10 Thu Jun 4 Phylogenetic inference