BICORN: An R package for integrative inference of de novo cis-regulatory modules
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AbstractGenome-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.
DescriptionAuthor correction at 10.1038/s41598-020-74149-y. "The original version of this Article contained errors. In the Abstract, “Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs).” now reads: “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.” In addition, in the Methods section, under the subheading ‘BICORN input’, “Binary TF-gene binding input is used because it is the signal format most commonly used by different resources.” now reads: “Binary TF-gene binding input is used because it is the signal format most commonly provided by different resources.” Finally, the Acknowledgements section in this Article was incomplete. “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.” now reads: “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. Note that open access publishing is supported by "VT Open Access Subvention Fund".” These errors have now been corrected in the HTML and PDF versions of the Article."
Keywordde novo cis-regulatory modules
Bayesian Inference of Cooperative Regulatory Network
R (Computer program language)
Identifier to cite or link to this itemhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85084787643&doi=10.1038%2fs41598-020-63043-2&partnerID=40&md5=b3300ea0c0dab19bcd0e8772b61b6fbc; http://hdl.handle.net/10713/12886
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