Advanced Topics in Computational Genomics
Course Number: 02-715
Research 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. Grading will be based on weekly write-ups for critiques 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.
Key Topics: Emerging topics in the field, subject to change each offering. Topics include (but are not limited to): alignment-free genomics, single-cell RNA-seq analysis, and immunogenomics.
It is expected that the students have basic background knowledge in both algorithms and genomics. Because the course focuses on recent work, it is expected that students will take time outside of class to fill in any knowledge gaps before each session.
This course is designed for advanced graduate students in CBD, primarily in their second year or beyond.
Prerequisite(s): 02-710 or equivalent
- Course material will consist of recently published work relating to the topics discussed in each module.