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,
- cancer genomics,
- phylogenetics, and
- population genetics.
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.
10% for each homework assignment.
The course home page can be found at http://noble.gs.washington.edu/~noble/genome541. A Canvas page is at https://canvas.uw.edu/courses/1372708.
All lectures will be delivered via Zoom, accessible via the Canvas page above. Lectures will be recorded and made available via Canvas.
Date Topic References Homework
Protein structure—Frank DiMaio Tue Mar 31 Protein structure Thu Apr 1 Molecular modeling—Macromolecular forcefields Homework #1 Tue Apr 7 Molecular modeling—optimization methods Thu Apr 9 Nucleic acid structure Homework #2 Epigenomics—Bill Noble Tue Apr 14 Transcription factor binding motifs compute-motif-pvalue.py small.txt (Noble 2009) Thu Apr 16 Supervised learning for transcription factor binding (Agius 2012) (Noble 2006) (Zhou 2015) Homework #3 Thu Apr 21 Semi-automated genome annotation and epigenomic imputation Fri Apr 23 Chromatin 3D architecture Homework #4 Cancer genomics—Gavin Ha Tue Apr 28 Introduction to cancer genome analysis Thu Apr 30 Probabilistic methods for mutation detection Homework #5 Tue May 5 Probabilistic methods for copy number alteration detection Thu May 7 Additional topics: structural variation, signature analysis Homework #6 Phylogenetics—Erick Matsen Tue May 12 Phylogenetics motivation and intro Thu May 14 Phylogenetics methods Homework #7 Tue May 19 Recombination and trees as data structures Thu May 21 Further topics Homework #8 Phylogenetics—Trevor Bedford Tue May 26 Thu May 28 Homework #9 Population genetics—Will DeWitt Tue June 2 Population genetics Thu June 4 Population genetics Homework #10