Carnegie Mellon University

Computational Genomics

Course Number: 02-710

Course Relevance: 

Graduate students in computational biology and graduate students who have interest in algorithm techniques in computational genomics.

Background Knowledge: 

Machine learning methods, probabilistic modeling, programming.

Key Topics: 

  • Sequence alignment
  • High-throughput sequencing data analysis
  • Analysis of gene expression data
  • Epigenetics and genome organization
  • Single cell data analysis
  • Complex biological networks
  • Application to specific biological processes and diseases


Semester(s): Spring
Units: 12
Prerequisite(s): Prerequisites / concurrent required class: 10-701 / 10-601 / 10-401 / 10-715 (Machine Learning), or an equivalent class.


Learning Resources: 

  • Piazza
  • Autolab

Learning Objectives

Dramatic advances in experimental technology and computational analysis are fundamentally transforming the basic nature and goal of biological research. The emergence of new frontiers in biology, such as systems biology, is demanding new methodologies that can confront quantitative issues of substantial computational sophistication. In this course we will discuss classical approaches and latest methodological advances in the context of genomics and systems biology.

From the computational side this course focuses on modern machine learning methodologies for computational problems in molecular biology.

Note: This course counts as a CSD Applications elective.

Assessment Structure: 

  • Homework assignments (40%)
  • Midterm exam (30%)
  • Project (25%)
  • Class participation (5%)