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

• Published in: Scientific reports 2020-05, Vol.10 (1), p.7960-7960, Article 7960
• Main Authors: Chen, Xi, Gu, Jinghua, Neuwald, Andrew F., Hilakivi-Clarke, Leena, Clarke, Robert, Xuan, Jianhua
• Format: Article
• Language: English
• Subjects: 38; 631/114/2114; 631/67/69; Bayes Theorem; Bayesian analysis; Binding sites; Cell Line; Computational Biology - methods; Gene expression; Gene regulation; Gene Regulatory Networks; Genomes; Humanities and Social Sciences; Humans; Localization; Mathematical models; multidisciplinary; Regulatory sequences; Regulatory Sequences, Nucleic Acid - genetics; Science; Science (multidisciplinary); Software
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-63043-2

Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites, based on which cis-regulatory modules (CRMs) can be inferred. 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 ( B ayesian I nference of CO operative R egulatory N etwork) 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 .