Skip to main content
Powered by ShareScore

Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

8,451

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

8,451 results for “Chromatin”

Learn how ShareScore rates datasets ↗
dryad36/100

The nanoCUT&RUN technique visualizes telomeric chromatin in Drosophila

<p>Advances in genomic technology lead to a more focused pattern for the distribution of chromosomal proteins and a better understanding of their functions. The recent development of the CUT&amp;RUN technique marks one of the important such advances. Here we develop a modified CUT&amp;RUN technique that we termed nanoCUT&amp;RUN, in which a high affinity nanobody to GFP is used to bring micrococcal nuclease to the binding sites of GFP-tagged chromatin proteins. Subsequent activation of the nuclease cleaves the chromatin, and sequencing of released DNA identifies binding sites. We show that nanoCUT&amp;RUN efficiently produces high quality data for the TRL transcription factor in Drosophila embryos, and distinguishes binding sites specific between two TRL isoforms. We further show that nanoCUT&amp;RUN dissects the distributions of the HipHop and HOAP telomere capping proteins, and uncovers unexpected binding of telomeric proteins at centromeres. nanoCUT&amp;RUN can be readily applied to any system in which a chromatin protein of interest, or its isoforms, carries the GFP tag. </p>

opencc-zeroAug 2022View details →
dryad36/100

Phosphorylated histone variant γH2Av is associated with chromatin insulators in Drosophila

<p>Chromatin insulators are responsible for orchestrating long-range interactions between enhancers and promoters throughout the genome and align with the boundaries of topologically associating domains (TADs). Here, we demonstrate an interaction between proteins that associate with the gypsy insulator and the phosphorylated histone variant H2Av (γH2Av), normally a marker of DNA double strand breaks. Gypsy insulator components colocalize with γH2Av throughout the genome, in polytene chromosomes and in diploid cells in which Chromatin IP data shows it is enriched at TAD boundaries. Mutation of insulator components prevents stable H2Av phosphorylation in polytene chromatin and phosphatase inhibition strengthens the association between insulator components and γH2Av and rescues γH2Av localization in insulator mutants. We also show that γH2Av is a component of insulator bodies, and that phosphatase activity is required for insulator body dissolution after recovery from osmotic stress. Together, our results indicate a novel mechanism linking the H2A variant γH2Av to insulator function. </p>

opencc-zeroSep 2022View details →
zenodo36/100

Multiscale Bayesian Simulations Reveal Functional Chromatin Condensation of Gene Loci

<p>Chromatin, the complex assembly of DNA and associated proteins, plays a pivotal role in orchestrating various genomic functions. To aid our understanding of the principles underlying chromatin organization, we introduce Hi-C metainference, a Bayesian approach that integrates Hi-C contact frequencies into multiscale prior models of chromatin. This approach combines both bottom-up (the physics-based prior) and top-down (the data-driven posterior) strategies to characterize the 3D organization of a target genomic locus. We first demonstrate the capability of this method to accurately reconstruct the structural ensemble and the dynamics of a system from contact information. We then apply the approach to investigate the Sox2, Pou5f1, and Nanog loci of mouse embryonic stem cells using a bottom-up chromatin model at 1kb resolution. We observe that the studied loci are conformationally heterogeneous and organized as crumpled globules, favoring contacts between distant enhancers and promoters. Using nucleosome-resolution simulations, we then reveal how the Nanog gene is functionally organized across the multiple scales of chromatin. At the local level, we identify diverse tetranucleosome folding motifs with a characteristic distribution along the genome, predominantly open at cis-regulatory elements and compact in between. At the larger scale, we find that enhancer-promoter contacts are driven by the transient condensation of chromatin into compact domains stabilized by extensive inter-nucleosome interactions. Overall, this work highlights the condensed, but dynamic nature of chromatin in vivo, contributing to a deeper understanding of gene structure-function relationships.</p>

opencc-by-4.0May 2024View details →
zenodo36/100

Chua et al. 2024 Kymographs of MeCP2 & TBLR1 on Chromatin

<p>Full kymographs of MeCP2 and TBLR1 on DNA and nucleosomes. Associated with manuscript by Chua et al. 2024.</p>

opencc-by-4.0Jun 2024View details →
zenodo36/100

Identifying genetic variants associated with chromatin looping and genome function

<p><span>Here<span> we present a comprehensive HiChIP dataset on na&iuml;ve CD4 T cells (nCD4) from 30 donors and identify QTLs that associate with genotype-dependent and/or allele-specific variation of HiChIP contacts defining loops between active regulatory regions (iQTLs). We observe a substantial overlap between iQTLs and previously defined eQTLs and histone QTLs, and an enrichment for fine-mapped QTLs and GWAS variants. Furthermore, we describe a distinct subset of nCD4 iQTLs, for which the significant variation of chromatin contacts in nCD4 are translated into significant eQTL trends in CD4 T cell memory subsets. Finally, we define connectivity-QTLs as iQTLs that are significantly associated with concordant genotype-dependent changes in chromatin contacts over a broad genomic region (e.g., GWAS SNP in the <em>RNASET2</em> locus). Our results demonstrate the importance of chromatin contacts as a complementary modality for QTL mapping and their power in identifying novel classes of QTLs linked to cell-specific gene expression and connectivity.</span></span></p> <p>&nbsp;</p> <p><span><span>This repository contains the source code, supplementary datasets for the manuscript (Nature Communications 2024).</span></span></p>

opencc-by-4.0Jul 2024View details →
zenodo36/100

Chromatin Accessibility Plays a Key Role in Selective Targeting of Hox Proteins

<p>Supplementary data tables for bioRxiv preprint &quot;Chromatin Accessibility Plays a Key Role in Selective Targeting of Hox Proteins &quot;</p> <p>All tables as&nbsp;Tab Delimited Text files</p>

opencc-by-4.0Nov 2018View details →
zenodo36/100

Chromatin regulation by Histone H4 acetylation at Lysine 16 during cell death and differentiation in the myeloid compartment

<p>Histone H4 acetylation at Lysine 16 (H4K16ac) is a key epigenetic mark involved in gene regulation, DNA repair and chromatin remodeling, and though it is known to be essential for embryonic development, its role during adult life is still poorly understood. Here we show that this lysine is massively hyperacetylated in peripheral neutrophils. Genome-wide mapping of H4K16ac in terminally differentiated blood cells, along with functional experiments, supported a role for this histone post-translational modification in the regulation of cell differentiation and apoptosis in the hematopoietic system. Furthermore, in neutrophils, H4K16ac was enriched at specific DNA repeats. These DNA regions presented an accessible chromatin conformation and were associated with the cleavage sites that generate the 50 kb DNA fragments during the first stages of programmed cell death. Our results thus suggest that H4K16ac plays a dual role in myeloid cells as it not only regulates differentiation and apoptosis, but it also exhibits a non-canonical structural role in poising chromatin for cleavage at an early stage of neutrophil cell death.</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2017View details →
zenodo36/100

Mapping cis-regulatory chromatin contacts in neural cells links neuropsychiatric disorder risk variants to target genes

<p>ATAC-seq peaks are in narrowPeak format.&nbsp;RNA-seq results are organized according to&nbsp;cell type.&nbsp;The normalized RPKM is reported for each gene in GENCODE 19. All data was mapped to hg19.</p>

opencc-by-4.0Jul 2019View details →
zenodo36/100

ROS-Specific Huntingtin Interactions: Chromatin Retention Assay Set-up

<p>Optimization of an assay to measure huntingtin chromatin retention in response to oxidative stress, using the YFP-tagged&nbsp;huntingtin-specific intrabody nucHCB2.</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

ROS-Specific Huntingtin Interactions: Chromatin Retention of Huntingtin in PARP KO Cells

<p>Huntingtin chromatin retention in response to oxidative stress in wild type, PARP1 knockout, PARP2 knockout, and PARP1/PARP2 knockout RPE1 cells.</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

ROS-Specific Huntingtin Interactions: Huntingtin chromatin retention dynamics by FRAP with veliparib

<p>Measurement of&nbsp;huntingtin chromatin recruitment dynamics by fluorescence recovery after photobleaching (FRAP) of the YFP-tagged huntingtin-specific intrabody, nucHCB2, under conditions of oxidative stress and PARP inhibition</p>

opencc-by-4.0Jul 2019View details →
zenodo36/100

ROS-Specific Huntingtin Interactions: Chromatin retention assay with huntingtin fragments containing PBM3

<p>Huntingtin amino acids 1790-1798 make up a potential PAR binding motif (PBM3). Two huntingtin fragments (1208-1810 and 1775-2413) were tested for chromatin retention upon oxidative stress.</p>

opencc-by-4.0Sep 2019View details →
zenodo36/100

Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_Figure5e_2

<p>Raw microscopy movies for smFRET experiments&nbsp;with various chromatin templates&nbsp;for Mivelaz M., et al, 2019&nbsp;<a href="https://doi.org/10.1016/j.molcel.2019.10.025">https://doi.org/10.1016/j.molcel.2019.10.025</a></p> <p>for&nbsp;Figure 5e (second part)</p> <p>see attached documentation for more details</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_Figure5f_1

<p>Raw microscopy movies for smFRET experiments&nbsp;with various chromatin templates&nbsp;for Mivelaz M., et al, 2019 <a href="https://doi.org/10.1016/j.molcel.2019.10.025">https://doi.org/10.1016/j.molcel.2019.10.025</a></p> <p>for&nbsp;Figure 5f (first part)</p> <p>see attached documentation for more details</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_Figure5f_2

<p>Raw microscopy movies for smFRET experiments&nbsp;with various chromatin templates&nbsp;for Mivelaz M., et al, 2019 <a href="https://doi.org/10.1016/j.molcel.2019.10.025">https://doi.org/10.1016/j.molcel.2019.10.025</a></p> <p>for&nbsp;Figure 5f (second part)</p> <p>see attached documentation for more details</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_FigureS6

<p>Raw microscopy movies for smFRET experiments&nbsp;with various chromatin templates&nbsp;for Mivelaz M., et al, 2019 <a href="https://doi.org/10.1016/j.molcel.2019.10.025">https://doi.org/10.1016/j.molcel.2019.10.025</a></p> <p>for&nbsp;FigureS6</p> <p>see attached documentation for more details</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_Figure5e_1

<p>Raw microscopy movies for smFRET experiments&nbsp;with various chromatin templates&nbsp;for Mivelaz M., et al, 2019</p> <p><a href="https://doi.org/10.1016/j.molcel.2019.10.025">https://doi.org/10.1016/j.molcel.2019.10.025</a></p> <p>&nbsp;</p> <p>for&nbsp;Figure 5e (first part)</p> <p>see attached documentation for more details</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_Fig.2,3,S3,S4,S7

<p>Raw microscopy movies for colocalization TIRF experiments (Rap1 binding) with various chromatin templates&nbsp;for Mivelaz M., et al, 2019 (<a href="https://doi.org/10.1016/j.molcel.2019.10.025">https://doi.org/10.1016/j.molcel.2019.10.025</a>)</p> <p>for&nbsp;Figures Fig.2,3,S3,S4,S7</p> <p>see attached documentation for more details</p>

opencc-by-4.0Jun 2019View details →
zenodo36/100

Decoding the Epigenetics and Chromatin Loop Dynamics of Androgen Receptor-Mediated Transcription

<p>This repository stores the datasets for the "Decoding the Dynamic Regulation and Chromatin Architecture of Androgen Receptor-mediated Gene Expression" paper.</p>

opencc-by-4.0Sep 2024View details →
zenodo36/100

Re-analysis of chromatin accessibility QTLs from the Kumasaka et al, 2018 study

<p>ATAC-seq data from&nbsp;<a href="https://doi.org/10.1038/s41588-018-0278-6">Kumasaka et al, 2018</a>&nbsp;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>

opencc-by-4.0Sep 2024View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record