02-715 Advanced Topics in Computational Genomics, Spring 2013
Course DescriptionResearch in biology and medicine is undergoing a revolution due to the availability of high-throughput technology for probing various aspects of a cell at a genome-wide scale. The next-generation sequencing technology is allowing researchers to inexpensively generate a large volume of genome sequence data. In combination with various other high-throughput techniques for epigenome, transcriptome, and proteome, we have unprecedented opportunities to answer fundamental questions in cell biology and understand the disease processes with the goal of finding treatments in medicine. The challenge in this new genomic era is to develop computational methods for integrating different data types and extracting complex patterns accurately and efficiently from a large volume of data. This course will discuss computational issues arising from high-throughput techniques recently introduced in biology, and cover very recent developments in computational genomics and population genetics, including genome structural variant discovery, association mapping, epigenome analysis, cancer genomics, and transcriptome analysis. The course material will be drawn from very recent literature. Class sessions will consist of lectures and discussions of recent papers led by the instructor. This course assumes a basic knowledge of machine learning and computational genomics (equivalent to 15-701 and 02-710).
GradingThe requirements of this course consists of short write-ups of summary/critique of the required readings for each class session, class participation, paper presentation, and final projects. The grading breakdown is as follows:
Class Participation: We expect students to participate in discussions of the papers in each class session.
Paper Presentation: We expect students to present the paper and lead the discussion in some of the class sessions.
Final Project: In a class project, students will develop new computational methods and test them on genomic data. Also, students can take existing methods (or extend the existing methods) and apply them to genomic data. Students can work on a project either on their own or as a team of up to two students. A one-page project proposal is due by Monday, March 18, in class, and students will give a short presentation on projects during the last week of the course. The final project report is due by Friday (May 10th) (dropbox in the course blackboard will be made available).