BICORN: An R package for integrative inference of de novo cis-regulatory modules

Xi Chen, Jinghua Gu, Andrew F. Neuwald, Leena Hilakivi-Clarke, Robert Clarke, Jianhua Xuan

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at

Original languageEnglish (US)
Article number7960
JournalScientific reports
Issue number1
StatePublished - May 14 2020
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by National Institutes of Health (NIH) grants CA149653 (to JX), CA164384 (to LHC) and CA149147 (RC), and by NIH-NIGMS grant R01GM125878 to AFN.

Publisher Copyright:
© 2020, The Author(s).


  • Bayes Theorem
  • Cell Line
  • Computational Biology/methods
  • Gene Regulatory Networks
  • Humans
  • Regulatory Sequences, Nucleic Acid/genetics
  • Software

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural


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