Course Description

A little over two decades since the first draft of the human genome sequence was published, high-throughput sequencing data is now routinely being generated at single cell level. These high-throughput data sets are being generated for multiple modalities, including transcriptomics, epigenomics, genomics, and proteomics. Significant advances have been made in the primary processing of these data sets that include mapping the data to reference genome and quantifying the data. However, the downstream analysis remains a major challenge, particularly in integrating data originating from multiple modalities.

Systematic integration of data originating from multiple modalities has the potential to decode novel cellular events that orchestrate gene regulation. Such integrative analyses can help elucidate molecular mechanisms that underlie the development or disease states of a cell or tissue. Here we look at strategies on how Transcriptomic, Genetic, and Epigenetic data sets can be integrated to decode the underlying biology of interest. 

Learner Outcomes

When you complete the course successfully, you will be able to:

• Locate public repositories that store quantified next-generation sequencing data for all three major modalities.
• Apply data science strategies to filter outliers or low-quality data for a given a quantified data set.
• Formulate integration strategies for data sets originating from at-least two distinct modalities.

Microcredential(s)

This course applies toward the Bioinformatics Endeavor digital badge.

Textbook Information