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.
890
datasets available to search
ShareScore release 0.9.0
Dataset results
890 results for “nucleosome”
An Integrated Structural Model of the DNA Damage Responsive H3K4me3 Binding WDR76:SPIN1 Complex with the Nucleosome
<p>Serial Capture Affinity Purification (SCAP) is a powerful method to isolate a specific protein complex. When combined with cross linking mass spectrometry (XL-MS) and computational approaches one can build an integrated structural model of the isolated complex. Here, we applied SCAP to dissect a subpopulation of WDR76 in complex with SPIN1, a histone marker reader that specifically recognizes trimethylated histone H3 lysine4 (H3K4me3). In contrast to a previous SCAP analysis of the SPIN1:SPINDOC complex, histones and the H3K4me3 mark were copurified with the WDR76:SPIN1 complex. Next, interaction network analysis of copurifying proteins and microscopy analysis revealed a potential role of the WDR76:SPIN1 complex in the DNA damage response. Since we detected an extensive number of cross-linked sites were found between WDR76, SPIN1, and histones, we first built an integrated structural model of the complex which revealed that SPIN1 recognized the H3K4me3 epigenetic mark while interacting with WDR76. Finally, we then used the powerful Integrative Modeling Platform to build a structural model of WDR76 and SPIN1 bound to the nucleosome.</p>
Subset of nucleosomal DNA sequences from mouse brain nucleus accumbens tissue (GEO dataset GSE54263)
<p>This dataset contains a subset of nucleosomal DNA sequences of +1 nucleosomes from mouse brain nucleus accumbens cells (NAC) used to analyze nucleosome positioning sequence (NPS) patterns in <a href="https://doi.org/10.1371/journal.pcbi.1007365">Pranckeviciene, Erinija and Hosid, Sergey and Liang, Nathan and Ioshikhes, Ilya (2020). Nucleosome positioning sequence patterns as packing or regulatory. In PLoS computational biology, 16 (1), pp. e1007365.</a></p> <ul> <li>controlm.fa.gz contains sequences of <strong>control</strong> mice (GSE54263 subset Con_H3 GSM1311267)</li> <li> resilientm.fa.gz contains sequences of mice <strong>resilient to social stress</strong> (GSE54263 subset Res_H3 GSM1311268)</li> <li> susceptiblem.fa.gz contains sequences of<strong> </strong>mice <strong>susceptible to social stress</strong> (GSE54263 subset Sus_H3 GSM1311269)</li> </ul> <p>This dataset originates from the GEO accession GSE54263 data from <a href="https://www.nature.com/articles/nm.3939">Sun H, Damez-Werno DM, Scobie KN, Shao NY et al. ACF chromatin-remodeling complex mediates stress-induced depressive-like behavior. <em>Nat Med</em> 2015 Oct;21(10):1146-53.</a></p>
Prediction of nucleosome dyads for the K562 cell line in the hg19 genome assembly
<p><strong><a href="https://andre-rendeiro.com/2015/05/12/predicting_dyads_from_mnase">Predicting dyads from MNase-seq data</a></strong></p> <p>I needed the location of nucleosomal dyads in the K562 cell line (ENCODE tier 1 line). Surprisingly, although plenty of MNase-seq data for that cell line is available, no nucleosome and dyad prediction exists.</p> <p><strong>Running NuMap</strong></p> <p>I found the <a href="http://www-hsc.usc.edu/~valouev/NuMap/NuMap.html">NuMap</a> software by Anton Valouev to do exactly what I intended.</p> <p>Since it is in a somewhat obscure page and this seemed to be the only place where this software was, I have <a href="https://github.com/afrendeiro/NuMap">uploaded it into a Github repository</a> for the sake of preservation (<a href="https://github.com/orphancode/NuMap">https://github.com/orphancode/NuMap</a>).</p> <p>Predicting dyads from MNase-seq data with NuMap seemed trivial: I downloaded the <a href="http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeSydhNsome/">K562 MNase-seq data set</a> (11 replicates ~85Gb!!), combined all replicates and ran NuMap on the data(instructions on the Github README).</p> <p>From NuMap output there are <a href="https://www.dropbox.com/s/asmp7bi40lrvtjb/K562_dyads.bed?dl=0">dyad positions in bed format</a> and you can also produce several metrics to evaluate how good the prediction was.</p> <p><strong>Distograms & phasograms</strong></p> <p>Valouev describes two measurements of the frequencies of distances between MNase-seq reads. The frequency of distances between reads mapping to opposite strands can be used to build a “distogram”, which ilustrates the expected nucleosome length (147 bp) - this is consistent across most eukaryotic cells. The frequency of distances between reads mapping to the same strand gives a measurement of the distance between nucleosomes, as they’re separated by some linker DNA - (Valouev calls this plot a “phasogram”). This measurement, on the other hand tends to be species and cell-type specific.</p> <p><strong>K562 predictions:</strong></p> <p>The expected 147 bp nucleosome length in K562 cells.</p> <p>The average distance between dyads in K562 cells seems to be 185 bp.</p> <p><strong>References:</strong></p> <p>Valouev, A., Johnson, S. M., Boyd, S. D., Smith, C. L., Fire, A. Z., Sidow, A. (2011). Determinants of nucleosome organization in primary human cells. Nature, 474(7352), 516–520. <a href="http://doi.org/10.1038/nature10002">http://doi.org/10.1038/nature10002</a></p>
Dynamic 1D search and processive nucleosome translocations by RSC and ISW2 chromatin remodelers
<p>Eukaryotic gene expression is linked to chromatin structure and nucleosome positioning by ATP-dependent chromatin remodelers that establish and maintain nucleosome-depleted regions (NDRs) near transcription start-sites. Conserved yeast RSC and ISW2 remodelers exert antagonistic effects on nucleosomes flanking NDRs, but the temporal dynamics of remodeler search, engagement and directional nucleosome mobilization for promoter accessibility are unknown. Using optical tweezers and 2-color single-particle imaging, we investigated the Brownian diffusion of RSC and ISW2 on free DNA and sparse nucleosome arrays. RSC and ISW2 rapidly scan DNA by one-dimensional hopping and sliding respectively, with dynamic collisions between remodelers followed by recoil or apparent co-diffusion. Static nucleosomes block remodeler diffusion resulting in remodeler recoil or sequestration. Remarkably, both RSC and ISW2 use ATP hydrolysis to translocate mono-nucleosomes processively at ~30 bp/sec for surprising distances on extended linear DNA. Processivity and opposing push-pull directionalities of nucleosome translocation shown by RSC and ISW2 shape the distinctive landscape of promoter chromatin.</p>
Chua et al. 2024 Kymographs of MeCP2 on Nucleosomes
<p>Full kymographs of MeCP2, truncations, and RTT mutants on nucleosomes. Associated with manuscript by Chua et al. 2024.</p>
Histone tail dynamics in partially disassembled nucleosomes during chromatin remodeling: Simulation dataset
<p>Dataset of molecular dynamics simulations of partially disassembled nucleosomes.</p> <p>- Input: Parameters and initial structures</p> <p>- Output: Trajectories</p> <p>NAMD 2.12 (multi-core with CUDA) was used for the simulations.</p>
Raw data of "Bridging of nucleosome-proximal DNA double-strand breaks by PARP2 enhances its interaction with HPF1"
<p>Raw data used in the following article:</p> <p>Bridging of nucleosome-proximal DNA double-strand breaks by PARP2 enhances its interaction with HPF1</p> <p>Guillaume Gaullier, Genevieve Roberts, Uma M. Muthurajan, Samuel Bowerman, Johannes Rudolph, Jyothi Mahadevan, Asmita Jha, Purushka S. Rae, Karolin Luger</p> <p>bioRxiv 846618; doi: <a href="https://doi.org/10.1101/846618">https://doi.org/10.1101/846618</a></p> <p>This includes:</p> <ul> <li>uncropped and unaltered images of all SDS-PAGE and native PAGE</li> <li>all size exclusion chromatograms and light scattering data</li> <li>raw data of all fluorescence polarization and FRET binding curves</li> <li>thermal shif assay raw data</li> </ul>
Cryo-EM/Cryo-ET raw images and tilt series for the figures in the paper entitled "Angle Between DNA Linker and Nucleosome Core Particle Regulates Array Compaction by Individual-Particle Cryo-Electron Tomography"
<p>Cryo-EM and cryo-ET raw images and tilt-series for the 3D reconstructions showed in the Figures of the paper entilted "Angle between DNA linker and nucleosome core particle regulates array compaction by individual-particle cryo-electron tomography"</p>
Molecular architecture of nucleosome remodeling and deacetylase sub-complexes by integrative structure determination
<p>Drawing on information from SEC-MALLS, DIA-MS, XLMS, negative-stain EM, X-ray crystallography, NMR spectroscopy, secondary structure predictions, and homology models, we applied Bayesian integrative structure determination to investigate the molecular architecture of three NuRD sub-complexes: MTA1-HDAC1-RBBP4 (MHR), MTA1<sup>N</sup>-HDAC1-MBD3<sup>GATAD2CC</sup> (MHM), and MTA1-HDAC1-RBBP4-MBD3-GATAD2A (NuDe). The present dataset pertains to the results of this study.</p>
Dynamic 1D search and processive nucleosome translocations by RSC and ISW2 chromatin remodelers
Open the record for dataset details and reuse information.
Data from: Diverse nucleosome site-selectivity among histone deacetylase complexes
<p>Histone acetylation regulates chromatin structure and gene expression and is removed by histone deacetylases (HDACs). HDACs are commonly found in various protein complexes to confer distinct cellular functions, but how the multi-subunit complexes influence deacetylase activities and site-selectivities in chromatin is poorly understood. Recent studies on the HDAC1 containing CoREST complex and acetylated nucleosome substrates revealed a notable preference for deacetylation of histone H3 acetyl-Lys9 vs. acetyl-Lys14 (M. Wu et al, 2018). Here we analyze the enzymatic properties of five class I HDAC complexes: CoREST, NuRD, Sin3B, MiDAC and SMRT with site-specific acetylated nucleosome substrates. Our results demonstrate that these HDAC complexes show a wide variety of deacetylase rates in a site-selective manner. A Gly13 in the histone H3 tail is responsible for a sharp reduction in deacetylase activity of the CoREST complex for H3K14ac. These studies provide a framework for connecting enzymatic and biological functions of specific HDAC complexes.</p>
ChIP-seq of plasma cell-free nucleosomes identifies cell-of-origin gene expression programs
<p>Genomic DNA is packed by histone proteins that carry a multitude of post-translational modifications that reflect cellular transcriptional state. Cell-free DNA (cfDNA) is derived from fragmented chromatin in dying cells, and as such it retains the histones markings present in the cells of origin. Here, we pioneer chromatin immunoprecipitation followed by sequencing of cell-free nucleosomes (cfChIP-seq) carrying active chromatin marks. Our results show that cfChIP-seq provides multidimensional epigenetic information that recapitulates the epigenetic and transcriptional landscape in the cells of origin. We applied cfChIP-seq to 268 samples including samples from patients with heart and liver pathologies, and 135 samples from 56 metastatic CRC patients. We show that cfChIP-seq can detect pathology-related transcriptional changes at the site of the disease, beyond the information on tissue of origin. In CRC patients we detect clinically-relevant, and patient-specific information, including transcriptionally active HER2 amplifications. cfChIP-seq provides genome-wide information and requires low sequencing depth. Altogether, we establish cell-free chromatin immunoprecipitation as an exciting modality with potential for diagnosis and interrogation of physiological and pathological processes using a simple blood test.</p>
STORM Data: Transcriptionally active chromatin loops contain both 'active' and 'inactive' histone modifications that exhibit exclusivity at the level of nucleosome clusters
<p>The dataset underlying the SMLM STORM super-resolution images of 'Transcriptionally active chromatin loops contain both ‘active’ and ‘inactive’ histone modifications that exhibit exclusivity at the level of nucleosome clusters'. See Biorxiv paper for details on sample preparation: <a href="https://www.biorxiv.org/content/10.1101/2023.09.03.555774v1.full.pdf">https://www.biorxiv.org/content/10.1101/2023.09.03.555774v1.full.pdf</a>, Pyranose Oxidase STORM buffer on Elyra 7 Zeiss Microscope, processed with Zen Black. Samples are named according to which figures they occur in the above paper.</p>
Nucleosome core particle MD simulations
<p>Gromacs topology files and xtc trajectory files of nucleosome core particule containing the alpha-palindromic sequence based on the 1KX5 PDB structure with 0.15 M of NaCl salt. Each trajectory is of 1 µs. Force field used: Amber14ff with IDP correction for the histone tails, parmbsc1, TIP3P water model.</p> <p>The letter in the name of the file are associated to a given simulation (1kx5-a_sol.top is related to 1kx5_sol-a.xtc).</p>
Single-particle tracking data for "CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning"
<p>This dataset contains all the raw SPT data reported in "­­­­CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning" in the form of SPT trajectories. The SPT trajectories are provided in two different formats for convenience: a CSV format and a Matlab format. Both formats are readable by Spot-On: https://spoton.berkeley.edu/</p> <p>The SPT data contains "fast tracking" spaSPT data and this data was analyzed using the Matlab version of Spot-On which can be found and downloaded at: https://gitlab.com/tjian-darzacq-lab/spot-on-matlab</p> <p> </p> <p>Full details about the Matlab and CSV formats are provided in the ReadMe files in the associated zip files.</p> <p>Please see the associated manuscript for a detailed description of how the data was acquired and analyzed.</p>
Experimental data and benchmarks used in the paper "Nucleosome Dynamics: A new tool for the dynamic analysis of nucleosome positioning"
<p>Experimental data used to illustrate the analysis with Nucleosome Dynamics pipeline. Three publicly available data sets were used:</p> <ol> <li> <p>Yeast metabolic cycle MNase-seq data downloaded from GEO under accession number GSE77631 corresponding to time points 9 and 12<br> Nocetti, N., and Whitehouse, I. (2016). Nucleosome repositioning underlies dynamic gene expression. Genes & Development 30, 660–672.</p> </li> <li> <p>MNase-seq data for S. cerevisiae cells synchronized in G1 and S phase, as described by Deniz (2016). Raw data available under accession number SAMEA2698380<br> Deniz, Ö., Flores, O., Aldea, M., Soler-López, M., and Orozco, M. (2016). Nucleosome architecture throughout the cell cycle. Scientific Reports 6, 19729.</p> </li> <li> <p>MNase-seq data for S. cerevisiae grown in different media: YPD, Gal, and EtOH. Data aligned to sacCer1 downloaded from GEO using accession numbers GSM351492, GSM351493, and GSM351494.</p> Kaplan N, Moore IK, Fondufe-Mittendorf Y, Gossett AJ et al. The DNA-encoded nucleosome organization of a eukaryotic genome. <em>Nature</em> 2009 Mar 19;458(7236):362-6. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/19092803">19092803</a></li> </ol> <p>Each tar file contains two folders:</p> <ul> <li>inputs: bam/RData files can be used to run Nucleosome Dynamics analyses. bigWig files contain nucleosome coverage and can be used to visualise in a genome browser. </li> <li>outputs: results from all analyses (nucleR, NFR, TSS, Periodicity, Stiffness, NucDyn)</li> </ul> <p> </p> <p>Simulated data used to benchmark nucleosome positioning by nucleR, and nucleosome dynamics by NucDyn, DANPOS and Dimnp.</p> <p><strong>Figure </strong><strong>2B: </strong>synthetic data simulated for comparison of nucleR and Danpos to detect a second family of nucleosomes. Each folder pX contains simulations when the second nucleosome is present in X% of the families.</p> <p><strong>Figure 2C</strong>: Distance between the dyads identified by nucleR and DANPOS to the dyad position in the true synthetic nucleosome map for fuzzy and well positioned nucleosomes.</p> <p><strong>Figure 2D</strong>: Synthetic data used to compute sensitivity of the EVICTION prediction for NucDyn, DANPOS and Dimnp. Evictions were simulated removing reads from a given percentage of families (10%, 20%, …, 90%) and were identified from DANPOS output as a nucleosome with point_log2FC < -1 and point_diff_FDR < 0.01 (point with highest difference in the two samples, as reported by the software), and with default parameters for Dimnp </p> <p><strong>Figure 2E</strong>: Synthetic data used to compute sensitivity of the SHIFT prediction. Shifts were introduced displacing reads from 1 to 5 DNA turns (i.e. 10-50 bp) and modifying different percentages of the families (10%, 20%, …, 90%). </p> <p>For each simulated data:</p> <ul> <li><em> *.RData</em> files can be used to run nucleR or NucDyn (*mod* corresponds to the modified reads: eviction or shift introduced)</li> <li><em>*.bed </em>files can be used to run DANPOS or Dimnp (*mod* corresponds to the modified reads: eviction introduced)</li> <li>results/ folder contains results from DANPOS</li> <li><em>NR.gff</em> contains the results from nucleR (*mod* corresponds to the results for modified reads: eviction or shift introduced)</li> <li><em>ND.gff</em> contains the results from NucDyn</li> <li><em>res_dimnp_*</em> contains the results from Dimnp</li> </ul> <p><strong>FigSupDanposShift:</strong> Synthetic data used to compute sensitivity of the SHIFT prediction for DANPOS. Shifts were introduced displacing reads from 1 to 5 DNA turns (i.e. 10-50 bp) and modifying different percentages of the families (10%, 20%, …, 90%) and were identified from DANPOS output as a nucleosome with treat2control_dis-10 larger than the given displacement and point_diff_FDR < 0.01 (point with highest difference in the two samples, as reported by the software).</p> <p>For each simulated data:</p> <ul> <li><em> *.bed</em> files contain the modified nucleosome positions</li> <li><em>results</em> folder contains output from DANPOS</li> </ul>
Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_Figure5e_2
<p>Raw microscopy movies for smFRET experiments with various chromatin templates 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 Figure 5e (second part)</p> <p>see attached documentation for more details</p>
Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_Figure5f_1
<p>Raw microscopy movies for smFRET experiments with various chromatin templates 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 Figure 5f (first part)</p> <p>see attached documentation for more details</p>
Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_Figure5f_2
<p>Raw microscopy movies for smFRET experiments with various chromatin templates 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 Figure 5f (second part)</p> <p>see attached documentation for more details</p>
Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_FigureS6
<p>Raw microscopy movies for smFRET experiments with various chromatin templates 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 FigureS6</p> <p>see attached documentation for more details</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research 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.
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.