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

Transcription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIP-seq profiling of multiple TFs makes it feasible to infer functional TF modules. However, when inferring TF modules based on...

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Autores principales:	Xi Chen, Andrew F Neuwald, Leena Hilakivi-Clarke, Robert Clarke, Jianhua Xuan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/8dab73ad1e7f4e9e9a60c5ae09697eda
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spelling	oai:doaj.org-article:8dab73ad1e7f4e9e9a60c5ae09697eda2021-12-02T19:57:23ZChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.1553-734X1553-735810.1371/journal.pcbi.1009203https://doaj.org/article/8dab73ad1e7f4e9e9a60c5ae09697eda2021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009203https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Transcription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIP-seq 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 ChIP-GSM 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.Xi ChenAndrew F NeuwaldLeena Hilakivi-ClarkeRobert ClarkeJianhua XuanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009203 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Xi Chen Andrew F Neuwald Leena Hilakivi-Clarke Robert Clarke Jianhua Xuan ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.
description	Transcription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIP-seq 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 ChIP-GSM 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.
format	article
author	Xi Chen Andrew F Neuwald Leena Hilakivi-Clarke Robert Clarke Jianhua Xuan
author_facet	Xi Chen Andrew F Neuwald Leena Hilakivi-Clarke Robert Clarke Jianhua Xuan
author_sort	Xi Chen
title	ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.
title_short	ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.
title_full	ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.
title_fullStr	ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.
title_full_unstemmed	ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.
title_sort	chip-gsm: inferring active transcription factor modules to predict functional regulatory elements.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/8dab73ad1e7f4e9e9a60c5ae09697eda
work_keys_str_mv	AT xichen chipgsminferringactivetranscriptionfactormodulestopredictfunctionalregulatoryelements AT andrewfneuwald chipgsminferringactivetranscriptionfactormodulestopredictfunctionalregulatoryelements AT leenahilakiviclarke chipgsminferringactivetranscriptionfactormodulestopredictfunctionalregulatoryelements AT robertclarke chipgsminferringactivetranscriptionfactormodulestopredictfunctionalregulatoryelements AT jianhuaxuan chipgsminferringactivetranscriptionfactormodulestopredictfunctionalregulatoryelements
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ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.

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