Computational Human Genetics Course Site
COMS W4995.001/E6998.005, Fall '07

Administration

When: Wednesday, 6:10pm to 7:25pm ; Fall 2007
Where: 644 MUDD517 Hamilton Note Change!
By who:Itsik Pe'er, office hours: Mon 9-10
Teaching assistant:Ninad Dewal, office hours: TBA
What for: 3 credit points
For who: Graduate/advanced undergraduate students of relevant fields. Although the course is listed in Computer Science, cross enrollment by students with biomedical background is encouraged.

Abstract

This course is intended to introduce students of both computational and bio-medical skill sets to current quantitative understanding of human genetics and prepare them to computational research in the field. Topics include: genetics of a single site, coalescence with recombination, history of humans, mapping rare mutations through linkage, mapping common variants through association, isolated and admixed populations, natural selection, copy number changes, model organisms, and genotyping technologies. The computational toolbox discussed includes parameter inference, likelihood analysis, hidden Markov and other graphical models, eigenvalue decompositions, and classification problems.

All course material is being accumulated on CourseWorks , so please see there for the most up-to-date content.
` Last Year's course material is also available, but beware that this year the material is more inclusive, both in terms of scope as well as foundations

FAQ

  1. Q: I'm a Computer Science student with no background in biology. Can I take the course?
    A: Sure. The necessary biological background will be given in condensed form at the first meeting, and then supplemented when necessary. The course is designed for a small, interactive class, with each student having enough weight to affect the level of background given.
  2. Q: I'm a student in Medical Informatics/Biological Sciences with no background in Computer Science. Can I take the course?
    A: Sure. You will need to accept some leaps of faith during classes, and you need some background in probability/statistics/biometry. The course is designed for a small, interactive class, with each student having enough weight to affect the level of background given.
  3. Q: What are the projects like?
    A: An example may be giving you a 5 year-old paper, asking you to read, understand, implement the computational method so that it can work on today's data (which is often orders of magnitude larger and more complex), analyze what you find, and write up what you find. Submission will include:
  4. Q: What are the office hours?
    A: See my contacts.
  5. Q: Is there a book with the course material?
    A: Sorry, but no. The course is given in this form for the first time, and material is being picked up here and there, much of it from the recent scientific literature. The slides for each lecture will include references.