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15,745
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Dataset results
15,745 results for “ChIP-seq”
Supporting data for "Software pipelines for RNA-Seq, ChIP-Seq and Germline Variant calling analyses in Common Workflow Language (CWL)"
<p>Datasets produced during the validation of CWL-based pipelines, designed for the analysis of data from RNA-Seq, ChIP-Seq and germline variant calling experiments. Specifically, the workflows were tested using publicly available High-throughput (HTS) data from published studies on Chronic Lymphocytic Leukemia (CLL) (accession numbers: E-MTAB-6962, GSE115772) and Genome in a Bottle (GIAB) project samples (accession numbers: SRR6794144, SRR22476789, SRR22476790, SRR22476791).</p> <p>The supporting data include:</p> <ul> <li>Differential transcript and gene expression results produced during the analysis with the CWL-based RNA-Seq pipeline</li> <li>Bigwig and narrowPeak files, differential binding results, table of consensus peaks and read counts of EZH2 and H3K27me3, produced during the analysis with the CWL-based ChIP-Seq pipeline</li> <li>VCF files containing the detected and filtered variants, along with the respective hap.py () results regarding comparisons against the GIAB golden standard truth sets for both CWL-based germline variant calling pipelines</li> </ul>
Classification dataset for ENCODE-Roadmap DNase-seq peaks and Transcription Factor ChIP-seq peaks
<p>Classification dataset for machine learning on epigenomic landscapes from ENCODE and Roadmap Epigenomics. The dataset includes the original peak files (BED format) for the DNase-seq and transcription factor (TF) ChIP-seq experiments that were used to label genomic regions as positives or negatives. The DNase-seq peak files along with processing details are in the file "encode-roadmap.DNase-seq.peaks.tar.gz". The TF ChIP-seq peak files along with processing details are in the file "encode.ChIP-seq.peaks.tar.gz". The processed dataset stored in hdf5 format files along with processing details are in the file "nn.encode-roadmap.hdf5_files.tar.gz".</p>
DOHH2 hg38 CTCF ChIP-seq Dataset filtered for Unique Multiread Mappability
<p>hg38 DOHH2 CTCF ChIP-seq Dataset filtered for Unique Multiread Mappability at a threshold of .75 using Umap</p>
DOHH2 hg38 H3K27ac ChIP-seq Dataset filtered for Unique Multiread Mappability
<p>hg38 DOHH2 H3K27ac ChIP-seq Dataset filtered for Unique Multiread Mappability at a threshold of .75 using Umap</p>
DOHH2 hg38 H3K4me3 ChIP-seq Dataset filtered for Unique Multiread Mappability
<p>hg38 DOHH2 H3K4me3 ChIP-seq Dataset filtered for Unique Multiread Mappability at a threshold of .75 using Umap</p>
DOHH2 hg38 H3K27me3 ChIP-seq Dataset filtered for Unique Multiread Mappability
<p>hg38 DOHH2 H3K27me3 ChIP-seq Dataset filtered for Unique Multiread Mappability at a threshold of .75 using Umap</p>
Test data for running snakePipes : ChIP-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the ChIP-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for human (<strong>hg38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
DOHH2 hg38 H3K4me1 ChIP-seq Dataset filtered for Unique Multiread Mappability
<p>DOHH2 hg38 H3K4me1 ChIP-seq Dataset filtered for Unique Multiread Mappability</p>
Dataset for EBAII practical session (ChIP-seq workflow)
<p>Dataset for EBAII practical session (ChIP-seq workflow)</p>
Practice dataset, CTCF mouse ChIP-seq, chr9
Open the record for dataset details and reuse information.
H3K4me ChIP-seq profiles, for NAR reviewers
Open the record for dataset details and reuse information.
Bhlhe40 Chip-seq
Open the record for dataset details and reuse information.
ChIP-seq - Definitive endoderm differentiation of human pluripotent stem cells in G1 phase
<p>Backup copy of the processed ChIP-seq data at http://ngs.sanger.ac.uk/production/endoderm/</p>
ChIP-seq from liver (ENCSR254YRM)
GEO Series GSE127549. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Histone ChIP-seq from stomach (ENCSR009RJD)
GEO Series GSE142879. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Histone ChIP-seq from BLaER1 (ENCSR239WVI)
GEO Series GSE257172. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Control ChIP-seq from HepG2 (ENCSR254UDV)
GEO Series GSE136482. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Vitamin D receptor (VDR) ChIP-Seq on human colonic organoids stimulated with 1,25(OH)2D3
GEO Series GSE206176. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
SS18::SSX redistributes BAF chromatin remodelers selectively to activate and repress transcription [ChIP-Seq, Hi-ChIP]
GEO Series GSE269770. Mus musculus. 71 samples. Type: Other.
Phosphorylation of p53 Serine 46 contributes to target gene selectivity of p53 (ChIP-seq)
GEO Series GSE21939. Homo sapiens. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.