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4 results for “Virtual ChIP-seq”
Datasets for predicting TF binding using Virtual ChIP-seq
<p>This repository contains datasets necessary for using the Virtual ChIP-seq software.</p> <p>Virtual ChIP-seq requires the following datasets to predict transcription factor binding:</p> <ul> <li> <p>chipExpDir_AtoH_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters A-H.</p> </li> <li> <p>chipExpDir_ItoZ_V1.0.0.tar.gz: Reference matrices of correlation between TF binding and gene expression for TFs starting with letters I-Z.</p> </li> <li> <p>refTables_V1.1.0.tar.gz: PhastCons genomic conservation, FIMO PWM scores for JASPAR motifs, and ChIP-seq data of ENCODE and Cistrome database.</p> </li> <li> <p>hg38_chrsize.tsv: Length of chromosomes in hg38</p> </li> <li> <p>trainedModels_V1.0.0.tar.gz: Virtual ChIP-seq scikit-learn trained models saved in joblib format</p> </li> <li> <p><CellType>.tar.gz: Pre-calculated matrices suitable for training with other algorithms or re-training with Virtual ChIP-seq.</p> </li> </ul> <p>Some predictive features of TF binding are the same in each cell type and are stored together for simplicity in refTables_V1.0.0.tar.gz. You can use datasets from other cell types (named here as <CellType>.tar.gz) for the purpose of re-training the model. The <CellType>.tar.gz files contain pre-calculated predictive features of transcription factor binding in 4 chromosomes (5, 10, 15, 20).</p> <p>These features include:</p> <ul> <li> <p>PhastCons genomic conservation</p> </li> <li> <p>FIMO score for sequence motifs of TF in the JASPAR database</p> </li> <li> <p>Chromatin accessibility</p> </li> <li> <p>TF binding in ENCODE + Cistrome DB datasets</p> </li> <li> <p>Virtual ChIP-seq expression score</p> </li> </ul> <p> </p>
Virtual ChIP-seq predictions of binding of 36 transcription factor in Roadmap Epigenomics Project tissues
<p>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.</p> <p>Tarball contains subfolders for each of the 36 TFs where Virtual ChIP-seq median MCC in validation cell types was > 0.3.</p> <p>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</p> <p>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).</p>
Virtual ChIP-seq predictions for TF binding in Cistrome and ENCODE-DREAM datasets
<p>Each gzipped tarball contains BED files of Virtual ChIP-seq posterior probabilities.</p> <p>Each BED file corresponds to binding of a TF in one chromosome (one of chr5, chr10, chr15, and chr20) in a validation cell type of Cistrome (virchipCistromePredictions_V2.0.0.tar.gz) or ENCODE-DREAM Challenge datasets (virchipDreamPredictions_V1.0.0.tar.gz).</p> <p> </p> <p>V2.0.0 update:</p> <ul> <li>We provided our predictions on ENCODE-DREAM Challenge validation chromosomes (chr1, chr8, and chr21) in virchipDreamPredictions_V2.0.0.tar.gz</li> <li>FOXA1 predictions for binding in ch5 and chr10 of liver, and CTCF predictions for binding in chr10 of MCF-7 were missing from virchipCistromePredictions_V1.0.0.tar.gz</li> </ul> <p> </p>
The ultra-sensitive Nodewalk technique identifies stochastic from virtual, population-based enhancer hubs regulating MYC in 3D: Implications for the fitness of cancer cells [ChIP-seq]
GEO Series GSE76048. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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