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1,636 results for “chromatin accessibility”
Chromatin accessibility data for the CRISPRai prediction algorithm implemented in crisprScore
<p>Chromatin accessibility data for the CRISPRai prediction algorithm implemented in crisprScore; see https://github.com/crisprVerse/crisprScore for more detail.</p> <p> </p>
ATAC-seq dataset: Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors
<p>The dataset encompasses raw sequencing reads, identified peaks, and regions of differential accessibility derived from ATAC-seq experiments conducted under various immune activation conditions. For additional technical details regarding data collection, please refer to the published source at https://doi.org/10.1093/jxb/erab373.</p>
SMAdd-seq: Probing chromatin accessibility with small molecule DNA intercalation and nanopore sequencing
<p>Studies of in vivo chromatin organization have relied on the accessibility of the underlying DNA to nucleases or methyltransferases, which is limited by their requirement for purified nuclei and enzymatic treatment. Here, we introduce a nanopore-based sequencing technique called Small-Molecule Adduct sequencing (SMAdd-seq), where we profile chromatin accessibility by treating nuclei or intact cells with a small molecule, angelicin. Angelicin reacts with thymine bases in linker DNA not bound to core nucleosomes after UV light exposure, thereby labeling accessible DNA regions. By applying SMAdd-seq in Saccharomyces cerevisiae, we demonstrate that angelicin-modified DNA can be detected by its distinct nanopore current signals. To systematically identify angelicin modifications and analyze chromatin structure, we developed a neural network model, NEural network for mapping MOdifications in nanopore long-reads (NEMO). NEMO accurately called expected nucleosome occupancy patterns near transcription start sites at both bulk and single-molecule levels. We observe heterogeneity in chromatin structure and identify clusters of single-molecule reads with varying configurations at specific yeast loci. Furthermore, SMAdd-seq performs equivalently on purified yeast nuclei and intact cells, indicating the promise of this method for in vivo chromatin labeling on long single molecules to measure native chromatin dynamics and heterogeneity.</p>
Oscillatory dynamics of mRNA metabolism and chromatin accessibility in mESCs during the cell cycle
<p>Processed single-cell RNAseq and multiome sequencing--single-nucleus RNAseq and ATACseq for mESCs</p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>scrna_deepdycle.h5ad</td> <td>single-cell RNAseq data, has information about unsplcied and spliced levels and the inferred cell cycle phase</td> </tr> <tr> <td>snrna_deepdycle_rep1.h5ad</td> <td>single-nulceus RNAseq data (replicate 1), has information about unsplcied and spliced levels and the inferred cell cycle phase</td> </tr> <tr> <td>snrna_deepdycle_rep2.h5ad</td> <td>single-nulceus RNAseq data (replicate 2), has information about unsplcied and spliced levels and the inferred cell cycle phase</td> </tr> <tr> <td>body_coverage_lif1.tsv.gz</td> <td>single nucleus ATACseq data (replicate 1), counts table of ATACseq peaks mapped to the genes</td> </tr> <tr> <td>body_coverage_lif2.tsv.gz</td> <td>single nucleus ATACseq data (replicate 2), counts table of ATACseq peaks mapped to the genes</td> </tr> <tr> <td>model_predictions.zip</td> <td>gene-wise predictions for gene expression--unspliced and spliced, and mRNA metabolism rates--synthesis, splicing, export, and degradation rates for single-cell and single-nucleus data</td> </tr> </tbody> </table>
Chromatin Accessibility Plays a Key Role in Selective Targeting of Hox Proteins
<p>Supplementary data tables for bioRxiv preprint "Chromatin Accessibility Plays a Key Role in Selective Targeting of Hox Proteins "</p> <p>All tables as Tab Delimited Text files</p>
Re-analysis of chromatin accessibility QTLs from the Kumasaka et al, 2018 study
<p>ATAC-seq data from <a href="https://doi.org/10.1038/s41588-018-0278-6">Kumasaka et al, 2018</a> was processed with the <a href="https://github.com/nf-core/atacseq/tree/2.1.2">nf-core/atacseq</a> v2.1.2 pipeline using Nextflow v23.09.3. We aligned raw ATAC-seq reads to the GRCh38 reference genome (Homo_sapiens.GRCh38.dna.primary_assembly.fa downloaded from Ensembl) with BWA v0.7.17. We called broad peaks with MACS2 v2.2.7.1 and defined consensus peaks as the union of all peaks that were present in at least 5% of the samples. We then quantified read overlaps with the set of consensus peaks with featureCounts v2.0.1. Finally, we normalised the read counts (counts per million) and then used the inverse normal transformation to standardise the data distribution.</p> <p>Genotype data for the 91 overlapping samples were downloaded from 1000 Genomes 30x on GRCh38 <a href="https://www.internationalgenome.org/data-portal/data-collection/30x-grch38">website</a>. Finally, we used the <a href="https://github.com/eQTL-Catalogue/qtlmap">eQTL-Catalogue/qtlmap</a> v24.01.1 workflow to perform chromatin accessibility QTL analysis. We set cis window size to 200,000 bp and excluded peaks that had less than 25 variants within that window. More details of the association testing workflow can be found <a href="https://doi.org/10.1371/journal.pgen.1010932">here</a>.</p>
DiCARN-DNase: Enhancing Cell-to-Cell Hi-C Resolution Using Dilated Cascading ResNet with Self-Attention and DNase-seq Chromatin Accessibility Data
<p>This repository contains the training, validation, and testing data for the DiCARN-DNase project. The GitHub repository is available via https://github.com/OluwadareLab/DiCARN_DNase</p>
Single cell ATAC-seq Mouse Kidney Data: Chromatin-accessibility estimation from single-cell ATAC data with scOpen
<p>We provide results regarding the bioinformatic analysis of scATAC-seq from mouse UUO kidney at different time points.</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
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Allele-specific chromatin accessibility for 23 cancer types and weight files for Regulome Wide Association-Studies (RWAS)
<p><strong>stratAS_results.tar.gz</strong></p> <p>This folder contains the results of analyses of allele-specific chromatin accessibility for 23 cancer types + pan-cancer using the stratAS software. The analyses identified allelically imbalanced genomic regions from 406 cancer ATAC-Seq samples. The files are named after each cancer type (or “pancancer” for the pan-cancer analysis) and contain the following columns:</p> <p>CHR - Chromosome<br> POS - Position of test<br> SNP RSID - ID of test SNP<br> P0 - Start of gene/peak<br> P1 - End of gene/peak<br> NAME - Name of gene/peak<br> CENTER - Center position of peak (or TSS for gene)<br> N.HET - Number of heterozygous individuals tested N.READS - Number of reads tested in total<br> ALL.AF - Allelic fraction estimate from beta binomial test across both conditions<br> ALL.BBINOM.P - Beta-binomial test for imbalance across both conditions<br> C0.AF - Allelic fraction estimate from condition 0<br> C0.BBINOM.P - Beta-binomial test for imbalance in condition 0<br> C1.AF - Allelic fraction estimate from condition 1<br> C1.BBINOM.P - Beta-binomial test for imbalance in condition 1<br> DIFF.BBINOM.P - Beta-binomial test for difference between conditions<br> IND.C0 - Number of each condition 0 individual included in this test (comma separated)<br> IND.C0.COUNT.REF - condition 0 REF allele counts of each individual included in this test (comma separated)<br> IND.C0.COUNT.ALT - condition 0 ALT allele counts of each individual included in this test (comma separated)<br> IND.C1 - Number of each condition 1 individual included in this test (comma separated)<br> IND.C1.COUNT.REF - condition 1 REF allele counts of each individual included in this test (comma separated)<br> IND.C1.COUNT.ALT - condition 1 ALT allele counts of each individual included in this test (comma separated)</p> <p>In our analysis, samples that belong to a cancer type that is analyzed for allelic imbalance are assigned to Condition 1 (C1) while samples belonging to the remaining 22 cancer types are assigned to Condition 0 (C0). For the pan-cancer analysis, samples from all 23 cancer types are assigned to C1. P-values denoting the significance of allelic imbalance in a given cancer type (or pan-cancer analysis) are listed in the column C1.BBINOM.P. P-values denoting the significance of differential allelic imbalance between a given cancer type and the remaining 22 cancer types are listed in column DIFF.BBINOM.P (set to NA for pan-cancer analysis since all samples are assigned to C1).</p> <p>More information about stratAS and its output format can be found at https://github.com/gusevlab/stratAS.</p> <p>The conducted analyses are described in detail in the methods section of Grishin D. and Gusev A. Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms (2022).</p> <p><strong>RWAS_weights.tar.gz</strong></p> <p>This folder contains weight files that can be used together with GWAS summary statistics to conduct Regulome Wide Association-Studies (RWAS) using the FUSION software package.</p> <p>Each weight file corresponds to a single accessible element in the FUSION format. Instructions on how to use FUSION for RWAS can be found at http://gusevlab.org/projects/fusion/. RWAS is conducted in the same manner as TWAS using these RWAS weights.</p> <p>The weight files were generated using a pan-cancer peak set. A BED file containing pan-cancer peaks can be found in the peaks_hg19 folder along with other BED files containing cancer-type specific peaks. Cancer-type specific peak files can be used to restrict the RWAS analysis to regulatory elements active in specific cancer types.</p> <p>The conducted analyses are described in detail in the methods section of Grishin D. and Gusev A. Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms (2022).</p>
Chromatin accessibility QTL lead variants in macrophages stimulated with IFNg and Salmonella
<p>Lead caQTL variants from RASQUAL and FastQTL analyses.</p>
Chromatin accessibility landscape of mouse early embryos revealed by single-cell NanoATAC-seq2
<p>The chromatin accessibility assay using scNanoATAC-seq2 was conducted on the Oxford Nanopore (ONT) long-read sequencing platform and focused on samples related to mouse preimplantation development. These samples included preimplantation embryos, ranging from the zygote to the late blastocyst, as well as oocytes, granulosa cells, and naive mouse embryonic stem cells. The embryos were derived from crosses of C57xDBA, DBAxC57, and C57xCAST. Additionally, cell lines from humans and mice were assayed during the technical testing phase.</p> <p> </p> <p><strong>Note: </strong>In this dataset, 'pre-TE' denotes early trophectoderm (early TE), while 'TE' signifies late trophectoderm (late TE), as outlined in the main article.</p>
BigWig and Bed Files for Chromatin Accessibility
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Quantitative dissection of transcription in development yields evidence for transcription factor-driven chromatin accessibility
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Profiling chromatin accessibility during stomatal development
GEO Series GSE190753. Arabidopsis thaliana. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Age-associated changes in murine endothelial gene expression and chromatin accessibility correlate with impaired blood-brain barrier integrity and elevated risk of intracerebral hemorrhage [RRBS]
GEO Series GSE218648. Mus musculus. 29 samples. Type: Methylation profiling by high throughput sequencing.
A Chromatin Accessibility Atlas of the Developing Human Telencephalon
GEO Series GSE149268. Homo sapiens. 40 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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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.
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.