Construction and analysis of an integrated regulatory network derived from High-Throughput sequencing data

Chao Cheng, Koon Kiu Yan, Woochang Hwang, Jiang Qian, Nitin Bhardwaj, Joel Rozowsky, Zhi John Lu, Wei Niu, Pedro Alves, Masaomi Kato, Michael Snyder, Mark Gerstein

Research output: Contribution to journalArticle

Abstract

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3′UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications.

Original languageEnglish (US)
Article numbere1002190
JournalPLoS computational biology
Volume7
Issue number11
DOIs
StatePublished - Nov 1 2011

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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    Cheng, C., Yan, K. K., Hwang, W., Qian, J., Bhardwaj, N., Rozowsky, J., Lu, Z. J., Niu, W., Alves, P., Kato, M., Snyder, M., & Gerstein, M. (2011). Construction and analysis of an integrated regulatory network derived from High-Throughput sequencing data. PLoS computational biology, 7(11), [e1002190]. https://doi.org/10.1371/journal.pcbi.1002190