Introduction to Computational Molecular Biology
GENOME 541Department of Genome Sciences
University of Washington
Spring Quarter, 2012
Course description:
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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.
Instructional staff
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.
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
Recommended: 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.
Recommended: Statistical Methods in Bioinformatics : An Introduction (Statistics for Biology and Health) by Warren J. Ewens, Gregory R. Grant; Springer, 2005. ISBN: 0387400826.
Course requirements
- 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.
- 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).
- 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
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None.
Course grade
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10% for each homework assignment.
Home page
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The course home page can be found at
http://noble.gs.washington.edu/~noble/genome541 .