Course unique #: 52716/52990
Lectures: Tuesday/Thursday 2:00 – 3:30 PM in WEL 3.260
Instructor: Edward Marcotte, email@example.com
- Office hours: Wednesdays 2:00 – 3:00 PM in MBB 3.148BA Phone: 471-5435
TA: John Woods, john.woods at marcottelab dot org
- TA Office hours: Tuesday/Friday 10:00 – 11:00 AM in MBB 3.128 Phone: 232-3919
Lectures & Handouts
Jan 22, 2013
- Python primer
- Problem Set 1, due Feb. 5, 2013
- E. coli genome
- T. volcanium genome
- 3 mystery genes (for Problem 5): Mgene1, Mgene2, Mgene3
Jan 17, 2013 - Newsworthy computational biology story of the week!
- Gymrek et al. (Supplement) show that genomic datasets are not as anonymous as we thought!]
- There are some associated commentaries, if you're curious: #1 2 #3
Syllabus & course outline
An introduction to computational biology and bioinformatics. The course covers typical data, data analysis, and algorithms encountered in computational biology. Topics will include introductory probability and statistics, basics of programming, protein and nucleic acid sequence analysis, genome sequencing and assembly, synthetic biology, analysis of gene expression data, data clustering, biological pattern recognition, and biological networks.
Open to graduate students and upper division undergraduates in natural sciences and engineering.
Prerequisites: Basic familiarity with molecular biology, statistics & computing, but realistically, it is expected that students will have extremely varied backgrounds.
Note that this is not a course on practical sequence analysis or using web-based tools. Although we will use a number of these to help illustrate points, the focus of the course will be on learning the underlying algorithms and exploratory data analyses and their applications.
Most of the lectures will be from research articles and handouts, with some material from the...
Recommended text (for sequence analysis): Biological sequence analysis, by R. Durbin, S. Eddy, A. Krogh, G. Mitchison (Cambridge University Press),
For non-molecular biologists, I highly recommend (really!) The Cartoon Guide to Genetics (Gonick/Wheelis)
For biologists rusty on their stats, The Cartoon Guide to Statistics (Gonick/Smith) is also very good.
Some online references:
An online bioinformatics course
Assorted bioinformatics resources on the web: #1, #2
Python coding for beginners
Beginning Python for Bioinformatics
Online probability texts: #1, #2, #3
No exams will be given. Grades will be based on 4 problem sets (given every 2 weeks and counting 15% each towards the final grade) and a course project (40% of final grade), which can be individual or collaborative. If collaborative, cross-discipline collaborations are encouraged. The course project will consist of a research paper or project on a bioinformatics topic chosen by the student (with approval by the instructor) containing an element of independent computational biology research (e.g. calculation, programming, database analysis, etc.). This will be turned in as a link to a web page.
The final project is due on April 30, 2013.
- If you don't have a unix/linux account to do the homework and/or project, send email to 'john.woods at marcottelab dot org'.
- CH391L/Connecting_Server (If you don't know how to use the account info, this are the instructions.)
- If you are having trouble transferring files from your computer to server, here are some GUI programs that can help you:
- How to make a web site for the final project
- Other relevant courses on campus:
- Geometric (Bio-) modeling and visualization
- Statistical and Discrete Methods for Scientific Computing
- BIO 337: Developmental Biology, emphasizing how to how to critically read and dissect papers.