02-223 How to Analyze Your Own Genome, Fall 2013

Course Information

  • Instructor: Seyoung Kim (Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University)
  • Co-instructor: Karen Thickman (Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University)

  • Location: GHC 4215
  • Time: Monday, Wednesday, & Friday 3:30-4:20pm
  • Office hours: Friday 4:30-5:30pm, GHC 7721 (Kim)
  • Tuesday 3:30-4:20pm, GHC 7403 (Thickman)
  • Course material: There is no required textbook for this course. Reading material will be drawn from various sources. Below is the list of optional textbooks.
    • Biological Sequence Analysis (Durbin, Eddy, Krogh, and Mitchison)
    • The Human Genome: A User's Guide (Richards & Hawley)
    • Computational Genome Analysis: An Introduction (Deonier, Tavare, and Waterman)
    • All of Statistics (Larry Wasserman)

Course Description

Do you want to know how to discover the tendencies hidden in your genome? Since the first draft of a human genome sequence became available about a decade ago, the cost of genome sequencing has decreased dramatically. It is expected that personal genome sequencing will become a routine part of medical examinations for patients in clinics for prognostic and diagnostic purposes. Personal genome information will also play an increasing role in lifestyle choices, as people take into account their own genetic tendencies. Commercial services such as 23andMe have already taken first steps in this direction. Computational methods for mining large-scale genome data are being developed to unravel the genetic basis of diseases and to assist doctors in clinics.

This course will introduce students to the biological, computational, and ethical issues that concern the use of personal genome information in health maintenance, medical practice, biomedical research, and policymaking. The course will focus on practical issues, using individual genome sequences (such as that of Nobel prize winner James Watson) and other population-level genome data. Without requiring any background in biological or computational sciences, the course will begin with an overview of topics from genetics, molecular biology, statistics, and machine learning that are relevant to the modern personal genome era. The class will then cover scientific issues such as how to discover your genetic ancestry, how to learn from genomes about the migration and evolution of the human population, and how natural selection shaped our genomes. The class will then discuss medical aspects such as how to predict whether you will develop diseases such as diabetes based on your own genome, how to discover disease-causing genetic mutations, and how the genetic information can be used to recommend clinical treatments. It will close with consideration of the complex policy issues that our society will face as this personal genomics revolution unfolds.

Grading

Grading will be based on biweekly homeworks (30%), a midterm (30%), a term paper (30%), and class participation (10%).
  • Bi-weekly homework problem sets will be posted on the course webpage. The completed homeworks should be submitted via blackboard.
  • Up to two students can work as a team to write a term paper. Each team will submit a short proposal and a full paper and give a short presentation in classroom:
    • A half-page proposal for a short description of the topic for your term paper should be submitted by midnight November 11 (Monday) via blackboard. It should contain a title, the names of the members in your team, and one or two paragraph description of the topic of your choice.
    • Term paper: A term paper should be no longer than 5 pages with 1 inch margin and 11pt font (single space). The term paper is due midnight December 12 (Thursday) and should be submitted via blackboard.
    • Presentation: Each team will give a short presentation (10-15 min) during the last week of the class.
  • Policy for late homeworks and term paper: The homeworks and term paper submitted one day late will receive 80% and those submitted two days late will receive 50% of the full grade.

Syllabus and Course Schedule (tentative)

Resources