• Login
    View Item 
    •   UMB Digital Archive
    • UMB Open Access Articles
    • UMB Open Access Articles 2020
    • View Item
    •   UMB Digital Archive
    • UMB Open Access Articles
    • UMB Open Access Articles 2020
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UMB Digital ArchiveCommunitiesPublication DateAuthorsTitlesSubjectsThis CollectionPublication DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    Display statistics

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

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Author
    Chen, X.
    Gu, J.
    Neuwald, A.F.
    Date
    2020
    Journal
    Scientific reports
    Publisher
    Nature Research
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1038/s41598-020-63043-2
    https://doi.org/10.1038/s41598-020-74149-y
    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.
    Description
    Author 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."
    Keyword
    de novo cis-regulatory modules
    Bayesian Inference of Cooperative Regulatory Network
    BICORN
    integrative inference
    R (Computer program language)
    Transcription Factors
    Bayes Theorem
    Identifier to cite or link to this item
    https://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
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41598-020-63043-2
    Scopus Count
    Collections
    UMB Open Access Articles 2020

    entitlement

    Related articles

    • De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets.
    • Authors: Niu M, Tabari ES, Su Z
    • Issue date: 2014 Dec 2
    • A graphical modelling approach to the dissection of highly correlated transcription factor binding site profiles.
    • Authors: Stojnic R, Fu AQ, Adryan B
    • Issue date: 2012
    • Predicting tissue specific cis-regulatory modules in the human genome using pairs of co-occurring motifs.
    • Authors: Girgis HZ, Ovcharenko I
    • Issue date: 2012 Feb 7
    • Identifying Functional Modules in Co-Regulatory Networks Through Overlapping Spectral Clustering.
    • Authors: Luo J, Yin Y, Pan C, Xiang G, Tu NH, Jiawei Luo, Ying Yin, Chu Pan, Gen Xiang, Nguyen Hoang Tu, Pan C, Xiang G, Yin Y, Luo J, Tu NH
    • Issue date: 2018 Apr
    • Bayesian network feature finder (BANFF): an R package for gene network feature selection.
    • Authors: Lan Z, Zhao Y, Kang J, Yu T
    • Issue date: 2016 Dec 1
    DSpace software (copyright © 2002 - 2021)  DuraSpace
    Quick Guide | Policies | Contact Us | UMB Health Sciences & Human Services Library
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.