Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules

Jing Chen, Zhen Hu, Mukta Phatak, Johannes M Freudenberg, John Reichard, Siva Sivaganesan and Mario Medvedovic

Laboratory for Statistical Genomics and Systems Biology
Department of Environmental Health,
University of Cincinnati College of Medicine,
3223 Eden Av. ML 56, Cincinnati OH 45267-0056

Abstract

Identification of genes that are directly regulated by a transcription factor (TF) requires joint considerations of TF binding data and the target gene’s expression changes. We developed a statistical framework for estimating genome-wide probabilities of TF-gene interactions modulating expression of targeted genes, and for using such probabilities as the genome-wide signatures of TF activity. Using such True REGulation (TREG) signatures we elucidate the ERα regulatory activity in producing complex diseases-related transcriptional profiles. Through a concordance analysis with transcriptional signatures of drug activity, we demonstrate that increase in statistical power associated with the use of TREG signatures can make the crucial difference in identifying key targets for treatment and identifying drugs to use for treatment.

Preprint (In press in PLoS Computational Biology)

  • Text
  • Figures
  • Supplemental Materials for the paper

  • R package treg
  • The Cytoscape environment for the network analysis of TREG signatures
  • GEO transcriptional profiles R dataset
  • CMAP transcriptional profiles R dataset
  • ENCODE TREG binding profiles R dataset
  • Browse concordances between ENCODE TF biniding signatures ,and disease and drug transcriptional signature using iLINCS genomics data portal
  • Contact

    mario.medvedovic@uc.edu