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8,451 results for “Chromatin”
Dynamics of CTCF and cohesin mediated chromatin looping revealed by live-cell imaging
<p><strong>Overview</strong></p> <p>This repository contains all the raw and processed trajectory data associated with “paper title”. In this ReadMe file we provide the following information:</p> <ul> <li>The cell lines and conditions used in this study</li> <li>A summary of how the data was collected</li> <li>The structure of the chromosome locus tracking data</li> </ul> <p><strong>Cell lines and conditions</strong></p> <p>In total, the dataset covers 12 experimental conditions representing the following cell lines and treatment conditions:</p> <ul> <li>C36</li> <li>C65</li> <li>C27</li> <li>CTCF-AID (untreated)</li> <li>CTCF-AID (2 hours AID)</li> <li>CTCF-AID (4 hours AID)</li> <li>RAD21-AID (untreated)</li> <li>RAD21-AID (2 hours AID)</li> <li>RAD21-AID (4 hours AID)</li> <li>WAPL-AID (untreated)</li> <li>WAPL-AID (4 hours AID)</li> <li>WAPL-AID (6 hours AID)</li> </ul> <p> </p> <p><strong>Data and data processing</strong></p> <p>Trajectories were obtained from 3D timeseries of mouse embryonic stem cell colonies in the conditions listed above using a LSM900 Airyscan 2 Zeiss microscope. For each movie we recorded 365 frames of 49.69 µm x 49.69 µm (584 x 584 pixels, pixel size: 0.085 µm by 0.085 µm), separated by an interval of 20 seconds for a total of just over 2 hours. 3D images were composed of 30 z-stacks separated by 0.25 µm, for a total height of 7.25 µm. Imaging was performed in two colors allowing the tracking of two arrays of fluorophores on Chromosome 18 near the <em>Fbn2</em> gene. In all conditions, the fluorophore arrays were separated by 515 kb (except the C27 clone where separation was 10 kb).</p> <p>The 3D image time series were processed using ConnectTheDots: <a href="https://github.com/ahansenlab/connect_the_dots">https://github.com/ahansenlab/connect_the_dots</a> to obtain paired trajectories of chromosome loci over time. The trajectories have been corrected for chromatic shifts and aberrations.</p> <p>Data are provided in an “unfiltered” format (meaning that individual dot localizations were not quality control filtered) , or a filtered format (the same data set, but having undergone quality control). The filtered (quality controlled) trajectory data was used for all the quantitative analyses in the article “”.</p> <p>File names are formatted follows.</p> <ul> <li>Quality controlled data have the structure: {Clone_and_condition_name}.tagged_set.tsv</li> <li>Unfiltered data have the structure: {Clone_and_condition_name}.unfiltered.tagged_set.tsv</li> </ul> <p>For example, for RAD21-AID tagged clone, for imaging performed after two hours of protein degradation, the quality-controlled file name is: RAD21_2_hr.tagged_set.tsv. Please note that for all no-treatment conditions, we used “0 hours” as the tag. Thus, the RAD21 (untreated) becomes RAD21_0_hr.tagged_set.tsv.</p> <p> </p> <p><strong>Structure of Data</strong></p> <p>The trajectory data are provided as tab-separated text files consisting of 10 columns. The column headers are:</p> <ul> <li>id: a unique dot pair index</li> <li>t: the frame in which the dots were localized</li> <li>x: x-coordinate of the dot in the EGFP channel (units in µm)</li> <li>y: y-coordinate of the dot in the EGFP channel (units in µm)</li> <li>z: z-coordinate of the dot in the EGFP channel (units in µm)</li> <li>x2: x-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>y2: y-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>z2: z-coordinate of the dot in the mScarlet channel (units in µm)</li> <li>dist: 3D distance between the dots across channels (units in µm)</li> <li>movie_index: an identifier used to link the dot pair back to the raw image timeseries.</li> </ul>
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>
Dataset for the article "Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion" by Roman Barth and Haitham Shaban
<p>The data set comprises all raw microscopy images and DFCC analyses as presented in </p> <p><strong>Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion</strong></p> <p>by Roman Barth and Haitham Shaban, published in Nucleus (https://doi.org/10.1080/19491034.2022.2088988)</p> <p>There are two folders for RNAPII and DNA each, one for the raw images and one for the processed DFCC data, supplied as .mat files.</p> <p>Every folder contains three sub-folders containing the data for the conditions: +Serum, -Serum, and +DRB.</p>
Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α-Fig. 2def
<p>smTIRF-FRET Data for Fig 2, for "Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α"</p>
Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α-Fig. 7cde
<p>smTIRF-FRET Data for Fig 7, for "Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α"</p>
Processed data for the study on "Chromatin 3D interactions mediate genetic effects on gene expression"
<p>This repository contains the processed data that was generated as part of the following study:</p> <p>Delaneau et al. (2019) <strong>Chromatin 3D interactions mediate genetic effects on gene expression.</strong></p> <p><em>Abstract:</em> Studying the genetic basis of gene expression and chromatin organization is key to characterize the effect of genetic variability on the function and structure of the human genome. Here, we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and inter-chromosomal levels within 12,583 Cis Regulatory Domains and 30 Trans Regulatory Hubs that highly reflect the local (i.e. Topologically Associating Domains) and global (i.e. open/close chromatin compartments) nuclear chromatin organization. These structures delimit cell type specific regulatory networks that control gene expression/co-expression and mediate the genetic effects of <em>cis</em>- and <em>trans</em>-acting regulatory variants on genes.</p> <p> </p> <p>This repository contains:</p> <ol> <li>Chromatin QTLs for H3K27ac, H3K4me1 and H3K4me3 discovered in 317 Lymphoblastoids Cell Lines (LCLs) and 78 Fibroblasts.</li> <li>Molecular QTLs affecting the activity and structure of Cis Regulatory Domains (CRDs) in LCLs.</li> <li>Basic information about the full set of genetic variants being analyzed in the study.</li> <li>The peak coordinates, their hierarchy based on inter-individual correlation and the CRD calls for both LCLs and Fibroblasts.</li> <li>The functional links discovered in LCLs between CRDs and genes.</li> <li>eQTLs for LCLs.</li> <li>A README file containing the description of the file format for each file.</li> </ol>
FitHiChIP: Identification of significant chromatin contacts from HiChIP data
<p>FitHiChIP is a computational method for identifying chromatin contacts among regulatory regions such as enhancers and promoters from HiChIP/PLAC-seq data.</p> <p><strong>Functionalities</strong> of FitHiChIP include:</p> <p>1) Calling significant interactions / loops / contacts from a HiChIP / PLAC-seq data </p> <p>2) Identifying peaks (enriched segments) from a HiChIP data (i.e. HiChIP peak caller)</p> <p>3) Finding differential loops among non-differential loci between two different categories of HiChIP samples, each with one or more replicates.</p> <p><strong>GitHub page</strong>: <a href="https://github.com/ay-lab/FitHiChIP">github.com/ay-lab/FitHiChIP</a></p> <p><strong>Documentation</strong>: <a href="https://ay-lab.github.io/FitHiChIP/">https://ay-lab.github.io/FitHiChIP/</a></p> <p><strong>Citation</strong>: Please check the above documentation regarding citation of FitHiChIP</p> <p>About this repository: All the data and results provided here correspond to the published manuscript. The file <strong>Data_Summary.xlsx</strong> summarizes for each figure, corresponding tables storing the related datasets.</p>
Suppl. Information to "The tropical coral Pocillopora acuta displays an unusual chromatin structure and shows histone H3 clipping plasticity upon bleaching"
<p><strong>Supplementary File 1:</strong> Multiple alignment for protein sequences of core histones with Pocillopora acuta, Pocillopora damicornis, Acropora digitifera, Nematostella vectensis, Hydra vulgaris, Schistosoma mansoni and Mus musculus. A. Histone H2A; B. Histone H2B; C. Histone H3; D. Histone H4. An asterisk (*) means that the amino acid is conserved between all species.</p> <p><strong>Supplementary File 2</strong>: Original (uncropped and unedited) images used for Figures 1 to 4.</p> <p><strong>Supplementary File 3:</strong> <em>P. acuta</em> nuclei and <em>Symbiodinium</em> count on a Thoma cell counting chamber done over three different nuclei extractions. For each extraction, two counts were performed. P. acuta nuclei were stained with Hoechst 33342 and display a blue fluorescence at 350 nm. Symbiodinium are not damaged by our extraction method and are not permeable to Hoechst. They display a red fluorescence because of their chlorophyl content. Observations were done on a Leica DMLB with objective PL Fluotar 40x and 100x. A text version of the data in the Excel file below.</p> <p>Extraction #1 replicate 1: 102 <em>P. acuta</em> nuclei (Blue) ; 2 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 112 <em>P. acuta</em> nuclei (Blue) ; 2 <em>Symbiodinium</em> (Red)</p> <p>Extraction #1 replicate 1: 42 <em>P. acuta </em>nuclei (Blue) ; 0 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 55 <em>P. acuta </em>nuclei (Blue) ; 1 <em>Symbiodinium</em> (Red)</p> <p>Extraction #1 replicate 1: 215 <em>P. acuta </em>nuclei (Blue) ; 3 <em>Symbiodinium</em> (Red)<br> Extraction #1 replicate 2: 257 <em>P. acuta</em> nuclei (Blue) ; 5 <em>Symbiodinium</em> (Red)</p> <p>Made at IHPE.</p>
Chromatin activity identifies differential gene regulation across human ancestries
<p>This repository contains data related to:</p> <p>Chromatin activity identifies differential gene regulation across human ancestries</p> <p>Kade P. Pettie, Maxwell Mumbach, Amanda J. Lea, Julien Ayroles, Howard Y. Chang, Maya Kasowski, Hunter B. Fraser</p> <p> </p>
Polymer simulations guide the detection and quantification of chromatin loop extrusion by imaging
<p><strong>Dataset description</strong></p> <p>This dataset contains coordinates of the two anchors of a 150 kb simulated loop (static imaging) and anchor-anchor distances of a 150 kb loop in simulations where the extruder was allowed to unbind from the polymer (dynamic imaging).</p> <p><strong>Dataset description - Static imaging</strong></p> <p>This dataset contains coordinates of the two anchors of a 150 kb simulated loop.</p> <p>The subfolder 'Free' contains coordinates from a polymer not submitted to loop extrusion (1,000 independent simulations). The subfolder 'Loop' contains loop anchor coordinates in a polymer submitted to loop extrusion (4,000 independent simulations). In simulations with extrusion, the polymer chain was simulated such as it went through 3 different states : i) Open state (absence of loops) ii) Extruding state where the loop size increases with time (anchor-anchor distance decreases) and iii) Closed state corresponding to a stable loop with the two anchors in contact.</p> <p> </p> <p><strong>Structure of data - Static imaging</strong></p> <p>In each .txt file, the first three columns correspond to the XYZ coordinates of the anchor (in µm), the fourth column indicates the state label (0=Open, 1=Extruding, 2=Closed). Each row corresponds to a simulation timepoint (2991 timepoints in polymers submitted to loop extrusion).</p> <p>The simulation ID is indicated at the end of each .txt file.</p> <p>The two anchors of the loop are bead #275 and bead #324.</p> <p> </p> <p><strong>Structure of data - Dynamic imaging</strong></p> <p>Each .txt file contains the anchor-anchor distance (in µm) as function of time for approximately 10,000 independent simulations. Each column is an independent simulation. Each row is a simulation timepoint (0.3 s / simulation unit). The different .txt files correspond to different localization errors, indicated in µm in XY and Z.</p> <p>'list_deb_closed_10000.p' is a dictionary whose keys are the simulation ID (corresponding to the columns of the .txt files), and the values are the frame at which extrusion begins.</p> <p>'list_end_closed_10000.p' is a dictionary whose keys are the simulation ID (corresponding to the columns of the .txt files), and the values are the frame at which extrusion ends.</p>
Abiotic stress mediated modulation of chromatin landscape in Arabidopsis thaliana
<p>This dataset include figures and supplementary material for the manuscript entitled<strong> </strong>"Abiotic stress mediated modulation of chromatin landscape in <em>Arabidopsis thaliana" </em>to be published in Journal of Experimental Botany special issue focused on Chromatin.</p> <p><strong>Supplementary File 1:</strong> Table describing read count, mapping percentage and genome coverage from each sample in FAIRE-seq and DNase-seq.</p> <p><strong>Supplementary File 2:</strong> List of DHSs obtained from control and stress subjected samples.</p> <p><strong>Supplementary File 3:</strong> List of FIRs obtained from control and stress subjected samples.</p> <p><strong>Supplementary File 4:</strong> List of uniquely merged OCRs with respective chromatin accessibility score in cold, heat, salt and drought stress.</p> <p><strong>Supplementary File 5:</strong> List of GO terms enriched in nrOCRs, SRCRs, and SACRs.</p> <p><strong>Supplementary File 6:</strong> List of GO terms enriched in overlapping nrOCRs, SRCRs, and SACRs.</p> <p><strong>Supplementary File 7:</strong> List of digital footprints (DFPs) obtained from nrOCRs regions of control-cold, control-heat, control-salt and control-drought pairs.</p> <p><strong>Supplementary File 8: </strong>Annotation details of the chromatin regions which were either found to be in state of accessible (CAS > 0.2) or inaccessible (CAS < -0.2) upon exposure to all of the stresses studied (heat, cold, salt and drought stress).</p> <p><strong>Supplementary Fig S1: Overlap of DHSs in control sample of present study with previously published studies.</strong></p> <p>A Venn diagram showing overlap of DNase hypersensitive sites (DHSs) found in control sample of present study and Zhang et al 2010 (<strong>A</strong>) and Sullivan et al 2014 (<strong>B</strong>). The statistical significance of overlap is calculate using hypergeometric Fischer`s exact test.</p> <p><strong>Supplementary Fig S2: Genomic locations of DHSs and FIRs</strong></p> <p>A line diagram representing the genomic location of unique DHSs and FIRs over each chromosome. DHSs/FIRs identified from each sample were merged to generate unique non-redundant subset of DHSs/FIRs before plotting over genome.</p> <p><strong>Supplementary Fig S3: Validation of correlation between OCRs and gene expression using microarray.</strong></p> <p>Box plot representing expression of genes (log10(normalised expression)) whose various structual elements fall in OCRs.</p> <p><strong>Supplementary Fig S4: First exons are highly enriched in both DHSs and FIRs</strong></p> <p> A bar plot showing presence of uFIRs, uDHSs, and ovOCRs in various positions of exon in Arabidopsis genes. The X-axis represent the exon number whereas Y-axis represent the fraction of OCRs found in each exon number.</p> <p><strong>Supplementary Fig S5: Validation of correlation between Ha-SACRs/Ha-SRCRs and gene expression using microarray.</strong></p> <p>Relative expression of genes (log2 fold change) corresponding to Ha-SACRs (Top) and (Ha-SRCRs (bottom) in cold (A), heat (B), salt (C) and drought (D) stress are plotted as box plot (p- value from Mann-Whitney test). To further compare RNA-seq data of salt stress with microarray, RNA-seq data was down-sampled to include genes which were also present in microarray data (E).</p> <p><strong>Supplementary Fig S6: Genomic location of SACRs and SRCRs found in Drought sample.</strong></p> <p>A snapshot of Integrative Genome Viewer (IGV) showing genomic location of stress activated chromatin regions (SACRs) and stress repressed chromatin region (SRCRs) in drought sample. The location of the centromere on each chromosome is shown as green bar IGV track.</p>
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>
Implications of the three-dimensional chromatin organization for genome evolution in a fungal plant pathogen
<p><span>The spatial organization of eukaryotic genomes is linked to their biological functions, although it is not clear how this impacts the overall evolution of a genome. Here, we uncover the three-dimensional (3D) genome organization of the phytopathogen <em>Verticillium dahliae</em>,<em> </em>known to possess distinct genomic regions, designated adaptive genomic regions (AGRs), enriched in transposable elements and genes that mediate host infection. Short-range DNA interactions form clear topologically associating domains (TADs) with gene-rich boundaries that show reduced levels of gene expression and reduced genomic variation. Intriguingly, TADs are less clearly insulated in AGRs than in the core genome. At a global scale, the genome contains bipartite long-range interactions, particularly enriched for AGRs and more generally containing segmental duplications. Notably, the patterns observed for <em>V. dahliae </em>are also present in other <em>Verticillium</em> species. Thus, our analysis links 3D genome organization to evolutionary features conserved throughout the <em>Verticillium</em> genus.</span></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>
Data from: Multiscale chromatin dynamics and high entropy in plant iPSC ancestors
<p>Plant protoplasts constitute the starting material to induce pluripotent cell masses <em>in vitro</em> competent for tissue regeneration. Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterized by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what are the factors influencing chromatin transitions during culturing is largely unknown. High-throughput imaging and a custom, supervised image analysis protocol extracting over 100 chromatin features unravelled a rapid, multiscale dynamics of chromatin patterns which trajectory strongly depends on nutrients availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating an intrinsic entropy as hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming <em>in vitro</em>.</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>
Datasets for chromatin hub prediction in six cell lines based on multiple genomic features
<p>Tables with features and classes for machine learning prediction of chromatin hubs. Genomic features include CTCF, EP300, H3K27me3, H3K36me3, H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K9me3, RAD21, RNAPol2, and RNA.Seq, while the classes are Hubs and Non-Hubs.</p> <p>The cell lines featured here are A549, H1ESC, HeLa, IMR90, K562, and MCF7. They happen to be the 6 cell lines out of 8 existing in our integrative database, GREG (https://doi.org/10.1093/database/baz162). The normalized read-coverages from features (variables) are mapped through genomic intervals of 2 Kbs, genome-wide. Such genomic intervals (bins), are classified as Hubs or Non-Hubs. Hubs are those bins with multiple chromatin interactions, including at least one long-range interaction (larger than 1Mb) or an inter-chromosomal interaction (tagged as Inf).</p> <p>Columns per table:<br> chr start end CTCF EP300 H3K27me3 H3K36me3 H3K4me1 H3K4me2 H3K4me3 H3K9ac H3K9me3 RAD21 RNA.Seq RNAPol2 Class</p> <p>Note that features may be inconsistent across different cell types, due to the availability of data. The BAM files have been sourced from ENCODE and NCBI repositories.</p> <p>The analysis following this data can be found at https://github.com/mora-lab/GREG-Hubs.</p>
Local chromatin context dictates the genetic determinants of the heterochromatin spreading reaction. Analysis Code, Numerical and Primary data.
<p>Uploaded under this Zenodo DOI is the following:</p> <p>1. the Analysis Code used for Flow Cytometry analysis in the paper, GO complex analysis (Figure 3) and Hit visualization (Figure 1, 2 S1, S4 Figs).</p> <p>2. The primary Flow Cytometry data from both the initial screen (ScreenFlowFCS) and validation experiments (ValidationFlowFCS) are included as .zip files.</p> <p>3. a .zip folder is uploaded that contains all the analysis code for the ChIP-Seq experiments. </p> <p>4. Excel worksheets that contain the numerical source data for all qPCR bar plots.</p>
ATP binding facilitates target search of SWR1 chromatin remodeler by promoting one-dimensional diffusion on DNA
<p>One-dimensional (1D) target search is a well-characterized phenomenon for many DNA-binding proteins but is poorly understood for chromatin remodelers. Herein, we characterize the 1D scanning properties of SWR1, a conserved yeast chromatin remodeler that performs histone exchange on +1 nucleosomes adjacent to a nucleosome-depleted region (NDR) at gene promoters. We demonstrate that SWR1 has a kinetic binding preference for DNA of NDR length as opposed to gene-body linker length DNA. Using single and dual color single-particle tracking on DNA stretched with optical tweezers, we directly observe SWR1 diffusion on DNA. We found that various factors impact SWR1 scanning, including ATP which promotes diffusion through nucleotide binding rather than ATP hydrolysis. A DNA-binding subunit, Swc2, plays an important role in the overall diffusive behavior of the complex, as the subunit in isolation retains similar, although faster, scanning properties as the whole remodeler. ATP-bound SWR1 slides until it encounters a protein roadblock, of which we tested dCas9 and nucleosomes. The median diffusion coefficient, 0.024 μm2/s, in the regime of helical sliding, would mediate rapid encounter of NDR-flanking nucleosomes at length scales found in cellular chromatin.</p>
Distinct chromatin signatures of DNA hypomethylation in aging and cancer (Datasets and additional files)
<p>Cancer is an aging-associated disease but the underlying molecular links between these processes are still largely unknown. Gene promoters that become hypermethylated in aging and cancer share a common chromatin signature in ES cells. In addition, there is also global DNA hypomethylation in both processes. However, any similarities of the regions where this loss of DNA methylation occurs is currently not well characterized, nor is it known whether such regions also share a common chromatin signature in aging and cancer.<strong> </strong>To address this issue we analysed TCGA DNA methylation data from a total of 2,311 samples, including control and cancer cases from patients with breast, kidney, thyroid, skin, brain and lung tumors and healthy blood, and integrated the results with histone, chromatin state and transcription factor binding site data from the NIH Roadmap Epigenomics and ENCODE projects. We identified 98,857 CpG sites differentially methylated in aging, and 286,746 in cancer. Hyper- and hypomethylated changes in both processes each had a similar genomic distribution across tissues and displayed tissue-independent alterations. The identified hypermethylated regions in aging and cancer shared a similar bivalent chromatin signature. In contrast, hypomethylated DNA sequences occurred in very different chromatin contexts. DNA hypomethylated sequences were enriched at genomic regions marked with the activating histone posttranslational modification H3K4me1 in aging, whilst in cancer, loss of DNA methylation was primarily associated with the repressive H3K9me3 mark.<strong> </strong></p>
ScienceDex guides
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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.