VirChIP Predictions for 36 TFs in Roadmap Epigenomics Project

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.

Description: 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).
Authors:
Mehran Karimzadeh, & Michael M. Hoffman
Lab: Hoffman
Release Date: 2018
Keywords: VirChIP, Virtual ChIP-seq, transcription factor binding, Roadmap Epigenomics

Citation

Karimzadeh, M., Hoffman, M.M. Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome. Genome Biol 23, 126 (2022). https://doi.org/10.1186/s13059-022-02690-2

36 Samples

Datatype:Virtual ChIP-seq
Species:Human
Disease Type:Other

Contact

This dataset is public.

Contact: Michael Hoffman

Contact email: Email