Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data

Abstract The epigenetics landscape of cells plays a key role in the establishment of cell-type specific gene expression programs characteristic of different cellular phenotypes. Different experimental procedures have been developed to obtain insights into the accessible chromatin landscape including...

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Autores principales:	Sascha Jung, Vladimir Espinosa Angarica, Miguel A. Andrade-Navarro, Noel J. Buckley, Antonio del Sol
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/38150884ca84451f9079585150bacf93
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spelling	oai:doaj.org-article:38150884ca84451f9079585150bacf932021-12-02T12:32:57ZPrediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data10.1038/s41598-017-04929-62045-2322https://doaj.org/article/38150884ca84451f9079585150bacf932017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04929-6https://doaj.org/toc/2045-2322Abstract The epigenetics landscape of cells plays a key role in the establishment of cell-type specific gene expression programs characteristic of different cellular phenotypes. Different experimental procedures have been developed to obtain insights into the accessible chromatin landscape including DNase-seq, FAIRE-seq and ATAC-seq. However, current downstream computational tools fail to reliably determine regulatory region accessibility from the analysis of these experimental data. In particular, currently available peak calling algorithms are very sensitive to their parameter settings and show highly heterogeneous results, which hampers a trustworthy identification of accessible chromatin regions. Here, we present a novel method that predicts accessible and, more importantly, inaccessible gene-regulatory chromatin regions solely relying on transcriptomics data, which complements and improves the results of currently available computational methods for chromatin accessibility assays. We trained a hierarchical classification tree model on publicly available transcriptomics and DNase-seq data and assessed the predictive power of the model in six gold standard datasets. Our method increases precision and recall compared to traditional peak calling algorithms, while its usage is not limited to the prediction of accessible and inaccessible gene-regulatory chromatin regions, but constitutes a helpful tool for optimizing the parameter settings of peak calling methods in a cell type specific manner.Sascha JungVladimir Espinosa AngaricaMiguel A. Andrade-NavarroNoel J. BuckleyAntonio del SolNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sascha Jung Vladimir Espinosa Angarica Miguel A. Andrade-Navarro Noel J. Buckley Antonio del Sol Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data
description	Abstract The epigenetics landscape of cells plays a key role in the establishment of cell-type specific gene expression programs characteristic of different cellular phenotypes. Different experimental procedures have been developed to obtain insights into the accessible chromatin landscape including DNase-seq, FAIRE-seq and ATAC-seq. However, current downstream computational tools fail to reliably determine regulatory region accessibility from the analysis of these experimental data. In particular, currently available peak calling algorithms are very sensitive to their parameter settings and show highly heterogeneous results, which hampers a trustworthy identification of accessible chromatin regions. Here, we present a novel method that predicts accessible and, more importantly, inaccessible gene-regulatory chromatin regions solely relying on transcriptomics data, which complements and improves the results of currently available computational methods for chromatin accessibility assays. We trained a hierarchical classification tree model on publicly available transcriptomics and DNase-seq data and assessed the predictive power of the model in six gold standard datasets. Our method increases precision and recall compared to traditional peak calling algorithms, while its usage is not limited to the prediction of accessible and inaccessible gene-regulatory chromatin regions, but constitutes a helpful tool for optimizing the parameter settings of peak calling methods in a cell type specific manner.
format	article
author	Sascha Jung Vladimir Espinosa Angarica Miguel A. Andrade-Navarro Noel J. Buckley Antonio del Sol
author_facet	Sascha Jung Vladimir Espinosa Angarica Miguel A. Andrade-Navarro Noel J. Buckley Antonio del Sol
author_sort	Sascha Jung
title	Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data
title_short	Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data
title_full	Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data
title_fullStr	Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data
title_full_unstemmed	Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data
title_sort	prediction of chromatin accessibility in gene-regulatory regions from transcriptomics data
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/38150884ca84451f9079585150bacf93
work_keys_str_mv	AT saschajung predictionofchromatinaccessibilityingeneregulatoryregionsfromtranscriptomicsdata AT vladimirespinosaangarica predictionofchromatinaccessibilityingeneregulatoryregionsfromtranscriptomicsdata AT miguelaandradenavarro predictionofchromatinaccessibilityingeneregulatoryregionsfromtranscriptomicsdata AT noeljbuckley predictionofchromatinaccessibilityingeneregulatoryregionsfromtranscriptomicsdata AT antoniodelsol predictionofchromatinaccessibilityingeneregulatoryregionsfromtranscriptomicsdata
_version_	1718393911630626816

Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data

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