02-715 Advanced Topics in Computational Genomics
12 units, Carnegie Mellon University
Research in biology and medicine is undergoing a revolution due to the availability of highthroughput technology for probing various aspects of a cell at a genomewide scale. The nextgeneration sequencing technology is allowing researchers to inexpensively generate a large volume of genome sequence data. In combination with various other highthroughput 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 highthroughput 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. Grading will be based on weekly writeups for ciritiques of the papers to be discussed in the class, class participation, and a final project. It assumes a basic knowledge of machine learning and computational genomics.
Prerequisites: none listed
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