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. 

What this course will not cover: This course in not intended to cover bioinformatics specific primary processing steps of sequencing data sets such as alignment, quantification etc. This course more focuses on  strategies for high level integration of disparate sequencing data sets with some programming in R involved.
 

Learner Outcomes

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

• Formulate integration strategies for data sets originating from two or more distinct modalities
• Identify the different application areas of next generation sequencing technologies. 
• Develop and implement integration strategies for data originating from single cell experiments. 

Microcredential(s)

This course applies toward the Bioinformatics Endeavor digital badge.

Textbook Information