///Comparative Transcriptomics
Comparative Transcriptomics2017-12-12T11:29:59+00:00

Comparative Transcriptomics

A typical gene expression data set records the expression level of every gene in a biological sample. Common analysis of such data involves

(1) demonstrating that many genes are differentially expressed in the sample, i.e., the biological condition has a “transcriptomic signature” that is then explored further,

(2) characterizing the differentially expressed genes (DEGs) that form the transcriptomic signature, and

(3) inferring components of the transcription regulatory networks (TRNs) that are responsible for the observed transcriptomic signature.

The KnowEnG framework will offer the user a variety of functionalities for these types of analyses and more, which are tested in the following project.

Molecular Roots of the Social Brain (MRSB)

This project examines whether a “dynamic genome” that is transcriptionally responsive to the social environment affects social behavior and if so, whether key brain molecular modules of social responsiveness are deeply conserved in evolution. We use the KnowEnG Analytics Suite to identify gene modules, key TFs, and parts of TRNs that are associated with SRGs. Identified TFs and TF-gene relationships are subjected to experimental follow-up. This project is supported by a grant from the Simons Foundation, and data generated in the project are subjected to advanced analysis in the KnowEnG framework.

Some of the key bioinformatics goals we are pursuing in this project include:

  • Identification of co-expressed gene clusters (modules) in a way that integrates gene annotations and gene-gene interactions present in the Knowledge Network.
  • Identification of co-expressed gene clusters that are shared by the three species (‘cross-species clustering’).
  • Characterization of differentially expressed gene sets using Random Walk algorithms applied to the Knowledge Network.
  • High dimensional regression algorithms to predict gene expression profiles, enabling reconstruction of gene regulatory networks.
  • Integration of epigenomic data sets with transcriptomic data to elucidate gene regulatory relationships.


Gene Robinson

Lisa Stubbs

Saurabh Sinha


Saurabh Sinha