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10.1038/s41598-017-04929-6 http://scihub22266oqcxt.onion/10.1038/s41598-017-04929-6 C5498635!5498635!28680085     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2017 ; 7 (ä): ä Nephropedia Template TP {{tp|p=28680085|t=2017. Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data |pdf=|usr=}}{{28680085}} gab.com Text Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28680085 #FOAvip 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. Twit Text FOAVip Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28680085 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28680085 #FOAvip English Wikipedia <ref>{{Cite pmid|28680085}}</ref> Prediction of Chromatin Accessibility in Gene-Regulatory Regions from Transcriptomics Data #MMPMID28680085 Jung S; Angarica VE; Andrade-Navarro MA; Buckley NJ; del Sol A Sci Rep 2017[]; 7 (ä): ä PMID28680085show ga 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. äPrediction of Chromatin Accessibility in Gene-Regulatory Regions from (epmc).pdf DeepDyve Pubget Overpricing