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752 results for “chromatin profiling”
Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis
<p>We performed comprehensive epigenetic profiling in immune cell samples from patients with ankylosing spondylitis and healthy controls. <br><br><em>Note: Due to Zenodo updating their maximum file limit to 100 files, version 4 </em>(v4) <em>of this archive has been split into 5 compressed archive (tar.gz) files containing all previous and additional files. <br><br></em>Version 4 of this archive (updated 03/06/2025) adds 4 files to the archive that were omitted in previous versions which have now been made available. These were: <em><br></em></p> <ul> <li>RNA_CD8_raw_counts.txt.gz</li> <li>RNA_CD8_normalised_counts.txt.gz</li> <li>RNA_CD14_raw_counts.txt.gz</li> <li>RNA_CD14_normalised_counts.txt.gz </li> </ul> <p><em>--------------------------------------------------------------------------------------------------------------------------</em></p> <p><strong>RNA-seq/ATAC-seq/ChIPm/eRNA: </strong>Raw and normalised count data for each gene or epigenetic peak in CD4+ T cells, CD8+ T cells, and CD14+ monocytes from AS patients and healthy controls. File name is in the format: "modality_cell-type_raw/normalised_counts.txt.gz". Table S2 shows which experiments were performed on which samples. </p> <p>This data can be found in the "Raw_Counts.tar.gz" and "Normalised_Counts.tar.gz" archives. </p> <p>--------------------------------------------------------------------------------------------------------------------------</p> <p><strong>ChromHMM: </strong>We used ChromHMM to integrate epigenomic data into a 14-emission state model detailing chromatin functionality in AS patients and healthy controls. ChromHMM filenames are in the format: "ChromHMM_sampleID_celltype_n.bed.gz" where n is the number of states in the ChromHMM emission model.</p> <p>This data can be found in the "ChromHMM_AS_HV.tar.gz" archive. </p> <p>--------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Capture-C: </strong>We performed Capture-C to detect chromosome looping interactions between gene promoters and SNPs associated with ankylosing spondylitis. Capture-C count data are shown in the format: "CaptureC_celltype_gene_Pro/SNP_normalised.unionbdg". We used PeakY to calculate a score for each interaction. PeakY scores are given in the following format: "PeakY_AS/HV_celltype_tier_chrloc_gene_Pro/SNP.txt". gene_Pro and gene_SNP relate to the baitsets given in Table S8.</p> <p>This data can be found in the "CapC_Count_Data.tar.gz" and "PeakY_regions.tar.gz" archives. </p>
Supporting data for "Chromatin conformation and histone modification profiling across human kidney anatomic regions"
<p>Here we deposit supporting data for our manuscript entitled "Chromatin conformation and histone modification profiling across human kidney anatomic regions".</p> <ul> <li>donor_info.pdf: Additional clinical information of the donor involved in the study</li> <li>Large zip files with names starting as "hic": Juicer Hi-C outputs aligned with hg38 genome <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>hg19_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg19 genome</li> <li>hg38_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg38 genome</li> <li>CUTRUN_peak_hg19.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg19 alignment</li> <li>CUTRUN_peak_hg38.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg38 alignment</li> <li>CUTRUN_bigwig_hg38.tar.gz: CUT&RUN bigwig outputs generated by hg38 alignment <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>CUTRUN_overlapped_peaks.xlsx: Overlaps between H3K27me3 and H3K4me3 in each anatomical region. Peaks were loaded from ‘.peaks.stringent.bed’ files in R and converted to GRanges objects using package ‘GenomicRanges’. R function ‘intersect’ was used to calculate the overlap between two GRanges objects, represented as each row in the table. <ul> <li>Here we list the details of overlaps between H3K27me3 and H3K4me3 in each anatomical region, as well as overlaps for each of the histone markers across anatomical regions.</li> </ul> </li> </ul>
CHROMATIN ACTIVATION PROFILING OF STEREOTYPED CHRONIC LYMPHOCYTIC LEUKEMIAS
<p>We used chromatin-immunoprecipitation followed by sequencing (ChIP-Seq) with an antibody for the H3K27ac (a bona fide histone mark for regulatory element activation) in sorted CLL cells from 46 CLL, including 21 cases from stereotyped subsets #1, #2, #4, and #8, and 15 normal B cell subpopulations. To understand their functional impact, we integrated our data with 16 RNA-seq data.</p> <p><strong>Files description:</strong><br> - <a href="https://zenodo.org/api/files/fb018e48-adb4-4750-a3e7-5188ac0a4123/H3K27ac_freq2_FDR_consensus_2022.txt?versionId=887e281e-526d-403d-a59d-4ac3e5773305">H3K27ac_freq2_FDR_consensus_2022.txt</a> count matrix from H3K27ac signals<br> - <a href="https://zenodo.org/api/files/fb018e48-adb4-4750-a3e7-5188ac0a4123/H3K27ac_FDR_consensus_norm.Combat_2022.txt?versionId=4a17abb5-0c95-42cc-b573-d11e0de2f484">H3K27ac_FDR_consensus_norm.Combat_2022.txt</a> count matrix from H3K27ac signals after batch effect correction<br> - <a href="https://zenodo.org/api/files/fb018e48-adb4-4750-a3e7-5188ac0a4123/RNAseq_norm.Combat_results.txt">RNAseq_norm.Combat_results.txt</a> count matrix from RNAseq after batch effect correction<br> - <a href="https://zenodo.org/api/files/fb018e48-adb4-4750-a3e7-5188ac0a4123/signature_RESAMPLING_ss8VSucll_2022.txt?versionId=806b1776-1571-4675-bc1c-3f0c29bf892e">signature_RESAMPLING_ss8VSucll_2022.txt</a> differentially acetylated regions between U-CLL and stereotyped subset #8 applying random sampling 100 times with a range of samples from 5 to n-1 in each category. The regions showing frequency >=20% were considered significant regions.</p>
Single-cell chromatin accessibility profiling reveals a self-renewing muscle satellite cell state
<p><span class="TextRun SCXW101911863 BCX0"><span class="NormalTextRun SCXW101911863 BCX0">A balance between self-renewal and differentiation is critical for the regenerative capacity of tissue-resident stem cells. In skeletal muscle, successful regeneration requires the orchestrated activation, proliferation, and differentiation of muscle satellite cells (</span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0">) that are normally quiescent. A subset of </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> undergoes self-renewal to replenish the stem cell pool, but the features that identify and define self-renewing </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> remain to be elucidated. Here, through single-cell chromatin accessibility analysis, we reveal the self-renewal versus differentiation trajectories of </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> over the course of regeneration in vivo. We identify </span><span class="SpellingError SCXW101911863 BCX0">Betaglycan</span><span class="NormalTextRun SCXW101911863 BCX0"> as a unique marker of self-renewing </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> that can be purified and efficiently contributes to regeneration after transplantation. We also show that SMAD4 and downstream genes are genetically required for self-renewal </span></span><span class="TextRun SCXW101911863 BCX0"><span class="NormalTextRun SCXW101911863 BCX0">in vivo</span></span><span class="TextRun SCXW101911863 BCX0"><span class="NormalTextRun SCXW101911863 BCX0"> by restricting differentiation. Our study unveils the identity and mechanisms of self-renewing </span><span class="SpellingError SCXW101911863 BCX0">MuSCs</span><span class="NormalTextRun SCXW101911863 BCX0"> while providing a key resource for comprehensive analysis of muscle regeneration.</span></span></p>
HG002 data for Profiling Chromatin Accessibility in Humans Using Adenine Methylation and Long-Read Sequencing
<p>This dataset includes 6mA frequency data for the HG002 native DNA (untreated) sample sequenced on nanopore r9.4.1.</p>
NA12878 and MCF7 data for Profiling Chromatin Accessibility in Humans Using Adenine Methylation and Long-Read Sequencing
<p>This dataset includes 5mC and 6mA frequency data for NA12878 and MCF7 EcoGII-treated chromatin samples sequenced on nanopore r9.4.1.</p>
Single-cell chromatin accessibility profiling reveals a self-renewing muscle satellite cell state
Open the record for dataset details and reuse information.
CRC tumor profiled in Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling
<p>Plain text files from mgatk output of CRC tumor sample originally described in "Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling" (https://doi.org/10.1038/s41587-020-0645-6). </p>
Selected data for Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling
<p>TF1 cell line (Figure 3) and CRC cancer (Figure 4) data from the "Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling" work. </p>
Low-cost high-resolution chromatin profiling with CUT&Tag
GEO Series GSE145187. Homo sapiens. 44 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Profiling chromatin accessibility during stomatal development
GEO Series GSE190753. Arabidopsis thaliana. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Unbiased profiling of clinical kinase inhibitors’ effects in activated macrophages using chromatin modifications as high-content readouts [ATAC-Seq]
GEO Series GSE218966. Mus musculus. 18 samples. Type: Other.
Genome-wide chromatin profiles of PBRM1 [ChIP-Seq]
GEO Series GSE152681. Homo sapiens. 77 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Chromatin and gene expression profile at single nuclei level of CD45+ leukocytes from the rhesus brain parenchyma
GEO Series GSE261968. Macaca mulatta. 8 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Simultaneous profiling of multiple chromatin proteins in the same cells
GEO Series GSE171554. Mus musculus. 21 samples. Type: Other.
Chromatin profile-based identification of a novel ER-positive breast cancer subgroup with reduced ER-responsive element accessibility
GEO Series GSE222116. Homo sapiens. 42 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
ATF3 drives senescence by reconstructing accessible chromatin profiles
GEO Series GSE157867. Homo sapiens. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Effect of Environmental Enrichment on Transcriptomic Profile and Chromatin Accessibility of Cortical Neurons
GEO Series GSE176435. Mus musculus. 43 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
High-throughput combinatorial indexing enables scalable single-cell chromatin accessibility profiling [PBMC]
GEO Series GSE123577. Homo sapiens. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Ontoepigenetic profile of mouse liver reveals dynamic regulation of chromatin during development
GEO Series GSE14620. Mus musculus. 13 samples. Type: Genome binding/occupancy profiling by genome tiling array; Methylation profiling by genome tiling array.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.
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