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ZENODO
Dataset . 2018
License: CC 0
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2018
License: CC 0
Data sources: Datacite
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Virtual ChIP-seq predictions of binding of 36 transcription factor in Roadmap Epigenomics Project tissues

Authors: Mehran Karimzadeh; Michael M. Hoffman;

Virtual ChIP-seq predictions of binding of 36 transcription factor in Roadmap Epigenomics Project tissues

Abstract

This dataset contains predictions of Virtual ChIP-seq for binding of 36 transcription factors in Roadmap Epigenomics dataset tissues with matched DNase-seq and RNA-seq data. Tarball contains subfolders for each of the 36 TFs where Virtual ChIP-seq median MCC in validation cell types was > 0.3. Each subfolder contains gzipped BED files. Each file is named as <Tissue>_<Age>_<TF>_<Accession>_Predictions.bed.gz. Columns correspond to Chromosome, Start, End, <Tissue>_<Age>_<TF>_<Accession>, Posterior probability You can use the posterior probabilities provided in Virchip_PosteriorCutoffs_V3.0.0.tsv. These are posterior probability cutoffs which maximized MCC in H1-hESC cell type, or are set to 0.4 if there was no ChIP-seq data of that TF in H1-hESC (0.4 is the mode of all optimal posterior probability cutoffs in H1-hESC).

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Keywords

Virtual ChIP-seq

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