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ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements

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dc.contributor.authorChen, Xien
dc.contributor.authorNeuwald, Andrew F.en
dc.contributor.authorHilakivi-Clarke, Leenaen
dc.contributor.authorClarke, Roberten
dc.contributor.authorXuan, Jianhuaen
dc.date.accessioned2022-02-22T17:29:59Zen
dc.date.available2022-02-22T17:29:59Zen
dc.date.issued2021-07-01en
dc.date.updated2022-02-22T17:29:54Zen
dc.description.abstractTranscription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIPseq profiling of multiple TFs makes it feasible to infer functional TF modules. However, when inferring TF modules based on co-localization of ChIP-seq peaks, often many weak binding events are missed, especially for mediators, resulting in incomplete identification of modules. To address this problem, we develop a ChIP-seq data-driven Gibbs Sampler to infer Modules (ChIP-GSM) using a Bayesian framework that integrates ChIP-seq profiles of multiple TFs. ChIP-GSM samples read counts of module TFs iteratively to estimate the binding potential of a module to each region and, across all regions, estimates the module abundance. Using inferred module-region probabilistic bindings as feature units, ChIP-GSM then employs logistic regression to predict active regulatory elements. Validation of ChIPGSM predicted regulatory regions on multiple independent datasets sharing the same context confirms the advantage of using TF modules for predicting regulatory activity. In a case study of K562 cells, we demonstrate that the ChIP-GSM inferred modules form as groups, activate gene expression at different time points, and mediate diverse functional cellular processes. Hence, ChIP-GSM infers biologically meaningful TF modules and improves the prediction accuracy of regulatory region activities.en
dc.description.versionPublished versionen
dc.format.extent22 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN e1009203 (Article number)en
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1009203en
dc.identifier.eissn1553-7358en
dc.identifier.issn1553-734Xen
dc.identifier.issue7en
dc.identifier.otherPCOMPBIOL-D-20-01578 (PII)en
dc.identifier.pmid34292930en
dc.identifier.urihttp://hdl.handle.net/10919/108817en
dc.identifier.volume17en
dc.language.isoenen
dc.publisherPLoSen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000677717200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBiochemical Research Methodsen
dc.subjectMathematical & Computational Biologyen
dc.subjectBiochemistry & Molecular Biologyen
dc.subjectCHROMATIN-STATE DISCOVERYen
dc.subjectSEQen
dc.subjectEXPRESSIONen
dc.subjectENHANCERSen
dc.subjectLINEAGEen
dc.subjectROLESen
dc.subject01 Mathematical Sciencesen
dc.subject06 Biological Sciencesen
dc.subject08 Information and Computing Sciencesen
dc.subjectBioinformaticsen
dc.subject.meshK562 Cellsen
dc.subject.meshChromatinen
dc.subject.meshHumansen
dc.subject.meshTranscription Factorsen
dc.subject.meshModels, Statisticalen
dc.subject.meshBayes Theoremen
dc.subject.meshComputational Biologyen
dc.subject.meshGene Expression Regulationen
dc.subject.meshEpigenesis, Geneticen
dc.subject.meshBinding Sitesen
dc.subject.meshRegulatory Sequences, Nucleic Aciden
dc.subject.meshGene Regulatory Networksen
dc.subject.meshEnhancer Elements, Geneticen
dc.subject.meshPromoter Regions, Geneticen
dc.subject.meshMCF-7 Cellsen
dc.subject.meshChromatin Immunoprecipitation Sequencingen
dc.titleChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elementsen
dc.title.serialPLOS Computational Biologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
dcterms.dateAccepted2021-06-20en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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