Abstract
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 (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.
Highlights
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
Among those tools focusing on the regulatory potential of multiple TFs, most only report strong regulators for each gene in order to avoid overfitting—which leads to incomplete regulatory network reconstruction
For each CRM, a small number of TFs are jointly represented so that overfitting effects are largely alleviated and both strong and weak regulators on each gene are captured. (BICORN is named after a mythical beast composed of a combination of creatures, because it models CRMs composed of a combination of TFs.)
Summary
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. 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. Conventional TF-gene regulatory network inference tools[12,13,14] fail in this regard either because they focus on individual TF-gene interactions rather than on multiple TFs associations Among those tools focusing on the regulatory potential of multiple TFs, most only report strong regulators for each gene in order to avoid overfitting—which leads to incomplete regulatory network reconstruction. To address these issues, here we describe BICORN (Bayesian Inference of COoperative Regulatory Network), a tool for functional CRM inference. We implemented BICORN in a R package and made it publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html
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