BICORN: An R package for integrative inference of de novo cis-regulatory modules. Academic Article uri icon

Overview

abstract

  • 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 https://cran.r-project.org/web/packages/BICORN/index.html.

publication date

  • May 14, 2020

Research

keywords

  • Computational Biology
  • Gene Regulatory Networks
  • Regulatory Sequences, Nucleic Acid

Identity

PubMed Central ID

  • PMC7224214

Scopus Document Identifier

  • 85084787643

Digital Object Identifier (DOI)

  • 10.1016/j.cell.2017.11.008

PubMed ID

  • 32409786

Additional Document Info

volume

  • 10

issue

  • 1