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
- protein structure,
- RNA-seq analysis,
- 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
Protein structure—Frank DiMaio Tue Mar 27 Protein structure Thu Mar 29 Molecular modeling—Macromolecular forcefields Homework #1 Tue Apr 3 Molecular modeling—optimization methods Thu Apr 5 Nucleic acid structure Homework #2 Proteomics—Brian Searle Tue Apr 10 Mass spectrometry and de novo sequencing Thu Apr 12 Database searching and E-value estimation Homework #3 Tue Apr 17 Mixture model analysis and target/decoy evaluation Thu Apr 19 Searching for post-translational modifications Homework #4 RNA-seq analysis—Sreeram Kannan Tue Apr 24 Transcriptome quantification and differential expression Thu Apr 26 Transcriptome assembly Homework #5 Tue May 1 Single-cell RNA-seq analytics: dimensionality reduction, clustering Thu May 3 scRNA-Seq: Lineage estimation, regulatory network inference Homework #6 Epigenomics—Bill Noble Tue May 8 Supervised learning from epigenomic data Thu May 10 Semi-automated genome annotation Homework #7 Tue May 15 Analysis of Hi-C data Thu May 17 3D modeling of DNA in the nucleus Homework #8 Phylogenetics—Erick Matsen Tue May 22 Phylogenetics motivation and intro Thu May 24 Phylogenetics methods Homework #9 Tue May 29 Theory and codon models Thu May 31 Further topics Homework #10