Carnegie Mellon University

Advanced Topics in Computational Genomics (offered infrequently)

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.


Semester(s): Spring
Units: 12


Learning Resources:

  • Piazza
  • Course material will consist of recently published work relating to the topics discussed in each module. 

Learning Objectives

This course is  primarily designed for graduate students to gain exposure to emerging topics in genomics that are not covered in existing course offerings. The topics presented here may overlap with the students ongoing research, but at least one topic should be novel. At the end of the course it is expected that students able to demonstrate some knowledge of the topics presented, the goal is for each student to feel comfortable working with and discussing  the topics being covered with anyone on the leading edge of the field.  Additionally, the course may expose students to topics that could be of research interest to them later in their career and spur ideas for ongoing research opportunities. We encourage the participants to integrate the projects with their own research if the opportunity arises.
No permission required for CBD Graduate students. Highly advanced undergraduates who have passed 02-510 and graduate students from other departments are welcome with instructor approval.