Computational Genetics
Instructor: Eleazar Eskin (
Mondays and Wednesdays 2:00-3:50
Office: Math Sciences Building 2925
Discussion Fridays 12-1:50 or 2:00-3:50
TA: Farhad Hormozdiari (
Office: Math Sciences Building 2915

Catalog Description:
While Humans differ by only 0.1% of their DNA sequence, these genetic differences account for a significant fraction of the observable differences between individuals including disease susceptibility. Understanding how genetic differences affect disease is one today's great challenges in medicine. This course is intended to introduce students to computational genetics and prepare them for computational interdisciplinary research in genetics.

The goal of the course is to expose students to interdisciplinary research and obtain experience in reading research papers outside computer science, as well as identifying, formulating and solving computational problems in an area outside computer science. NO BACKGROUND IN BIOLOGY IS NECESSARY OR REQUIRED.

Topics include introduction to genetics, identification of genes involved in disease, inferring human population history, technologies for obtaining genetic information and genetic sequencing. Focus on formulating interdisciplinary problems as computational problems and then solving these problems using computational techniques. The computational techniques discussed include techniques from statistics and computer science. The course is intended for both students in engineering as well as students from the biological sciences and medical school.

Recommended Text:
Introduction to R
(for those students who need a stronger background in R)

Statistics Using R with Biological Examples
(more background in R)

Grading Basis:
Students are graded primarily on the basis of the quality of their regular problem sets, a midterm, a non- cumulative final exam, completion of paper responses, and a final project. Graduate students are expected to complete a more significant final project and answer more questions on each problem set and exam. The course will require the reading of a few research papers and responses to the papers and/or relevant seminars will be required as part of the homework assignments.
Grade Breakdown: Homeworks 20%. Midterm Exam 20%. Final Exam 20% Final Project 30%. Paper Responses 10%.

Students taking the course should be familiar with any programming language and have completed a statistics course.

Course Syllabus:
Coming Soon.

Assignment Submission Policy: All assignments must be submitted by a hard copy to TA’s office by 4 pm on the day they are due. Late assignments will be penalized 5% per day. Barring any extreme circumstances, extensions will be considered only if requested 24 hours in advance of the deadline.
Coming Soon.