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2,207 results for “epigenomics”
Crossreactive probes on Illumina DNA methylation arrays: a large study on ALS shows that a cautionary approach is warranted in interpreting epigenome-wide association studies
<p>Data corresponding to the paper "Crossreactive probes on Illumina DNA methylation arrays: a large study on ALS shows that a cautionary approach is warranted in interpreting epigenome-wide association studies."<br> <br> Corresponding scripts can be found at: <a href="https://github.com/pjhop/dnamarray_crossreactivity">https://github.com/pjhop/dnamarray_crossreactivity</a><br> All downstream analyses in <a href="https://github.com/pjhop/dnamarray_crossreactivity/blob/master/analysis/c9_analysis.Rmd">c9_analysis.Rmd</a> and in<a href="https://github.com/pjhop/dnamarray_crossreactivity/blob/master/analysis/supplementary_note.Rmd"> supplementary_note.Rmd</a> can be reproduced using the deposited data as follows:</p> <ul> <li>Clone the dnamarray_crossreactivity repository: < git clone https://github.com/pjhop/dnamarray_crossreactivity.git ></li> <li>Download the data ('data.zip') and place it in the 'dnamarray_crossreactivity' folder.</li> <li>Unzip the data.zip folder</li> </ul> <p>Scripts used to generate the data in each subdirectory can be found at:</p> <ul> <li>data/processed/c9_matches/: <a href="https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/c9_matches">https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/c9_matches</a></li> <li>data/output/ewas/: <a href="https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/ewas">https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/ewas</a></li> <li>data/output/figs/: empty folder, running 'c9_analysis.Rmd' will save figures here.</li> <li>data/misc/: <a href="https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/other">https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/other</a></li> <li>data/extdata: <ul> <li>Zhou <em>et al.</em> annotations (EPIC.hg19.manifest.tsv.gz, HM450.hg19.manifest.pop.tsv.gz, HM450.hg19.manifest.tsv.gz) were downloaded from: <a href="https://zwdzwd.github.io/InfiniumAnnotation">https://zwdzwd.github.io/InfiniumAnnotation</a> (downloaded at 17/09/2020)</li> <li>Naeem <em>et al.</em><em> </em>data (12864_2013_7006_MOESM2_ESM.csv) was downloaded from: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3943510/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3943510/</a></li> <li>Chen <em>et al.</em> data (48639-non-specific-probes-Illumina450k.xlsx) was downloaded from <a href="https://github.com/Jfortin1/funnorm_repro/blob/master/bad_probes/48639-non-specific-probes-Illumina450k.xlsx">https://github.com/Jfortin1/funnorm_repro/blob/master/bad_probes/48639-non-specific-probes-Illumina450k.xlsx</a></li> <li>The anno_450k.txt.gz and anno_EPIC.txt.gz are subsets of the annotation files included in the following package respectively: <a href="https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylation450kanno.ilmn12.hg19.html">https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylation450kanno.ilmn12.hg19.html</a> and <a href="https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylationEPICanno.ilm10b2.hg19.html">https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylationEPICanno.ilm10b2.hg19.html</a></li> </ul> </li> <li> data/genome_bs: Scripts used to generate these data can be found at <a href="https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg19.R">https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg19.R</a> and <a href="https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg38.R">https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg38.R</a> .</li> <li> data/raw: Individual-level data is available upon access at: <a href="https://ega-archive.org/studies/EGAS00001004587">https://ega-archive.org/studies/EGAS00001004587</a></li> </ul>
Data and code for the publication "DNA methylation underpins the epigenomic landscape regulating genome transcription in Arabidopsis"
<p>The zipped file of this repository contains code and data to reproduce the results of the publication:</p> <p>Zhao et al, DNA methylation underpins the epigenomic landscape regulating genome transcription in Arabidopsis. Genome Biology (2022). </p> <p>All sequence data have been deposited in NCBI GEO accession codes GSE183987 and GSE169497.</p> <p> </p> <p>Please see the README document for detailed:</p> <p>- Descriptions of the code and data provided</p> <p>- Lists of the required dependencies</p>
Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome
<p>This is a comprehensive collection of diverse epigenomic profiling data in 100-bp resolution with full genome-wide coverage. The datasets are processed from raw read count data collected from five types of sequencing-based assays collected by the Encyclopedia of DNA Elements (ENCODE, <a href="http://www.encodeproject.org">http://www.encodeproject.org</a>) consortium. A total of 6,305 alignment profiles from various high-throughput sequencing assays available on the ENCODE database were preprocessed and filtered according to ENCODE’s data standard</p>
Virtual ChIP-seq predictions of binding of 36 transcription factor in Roadmap Epigenomics Project tissues
<p>This dataset contains predictions of Virtual ChIP-seq for binding of 36 transcription factors in Roadmap Epigenomics dataset tissues with matched DNase-seq and RNA-seq data.</p> <p>Tarball contains subfolders for each of the 36 TFs where Virtual ChIP-seq median MCC in validation cell types was > 0.3.</p> <p>Each subfolder contains gzipped BED files. Each file is named as <Tissue>_<Age>_<TF>_<Accession>_Predictions.bed.gz. Columns correspond to Chromosome, Start, End, <Tissue>_<Age>_<TF>_<Accession>, Posterior probability</p> <p>You can use the posterior probabilities provided in Virchip_PosteriorCutoffs_V3.0.0.tsv. These are posterior probability cutoffs which maximized MCC in H1-hESC cell type, or are set to 0.4 if there was no ChIP-seq data of that TF in H1-hESC (0.4 is the mode of all optimal posterior probability cutoffs in H1-hESC).</p>
Meta-analysis results of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight
<p>Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (P<sub>Bonferroni</sub><1.06x10<sup>-7</sup>).</p>
Opioid medication use and blood DNA methylation: epigenome-wide association meta-analysis
<p>We conducted the first large-scale epigenome-wide meta-analysis of blood DNA methylation and recent use of opioid medications. There were five participating studies (10,842 individuals; 9,886 European ancestry and 956 African ancestry participants) including four that used the newer Illumina EPIC/850K array and one that used the older Illumina 450K array. We identified novel loci differentially methylated in relation to opioid medication use.</p>
Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures
<p>Processed data associated with the manuscript DOI: <a href="https://doi.org/10.1016/j.xgen.2023.100421" target="_blank" rel="noopener">10.1016/j.xgen.2023.100421 </a></p> <p>Analysis code on GitHub: <a href="../doi/10.5281/zenodo.8253917" target="_blank" rel="noopener">10.5281/zenodo.8253917</a></p> <p> </p>
Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation"
<p>Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation". All nuclei passed QC and were corrected for background noise using cellBender. Files are in R objects saved in RDS (R Data Serialization) format. This repo contains one Seurat v4 object and one gene-by-cell raw RNA count matrix in sparse matrix format (dgCMatrix).</p>
Convolutional Neural Net (CNN) models for epigenomic landscapes in epidermal differentiation - Basset architecture, classification and regression
<p>Deep learning models trained on epigenomic landscapes in keratinocyte differentiation. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4062509. The file `nn.ggr.models.basset.clf.tar.gz` contains 10 cross-validated models that were pretrained using ENCODE-Roadmap trained model weights as initialization weights and also 10 cross-validated models that were initialized with random weights. Similarly, the file `nn.ggr.models.basset.regr.tar.gz` contains 10 cross-validated models that were pretrained using the classification model weights as initialization weights and also 10 cross-validated models that were initialized with random weights.</p>
EpiScanpy: integrated single-cell epigenomic analysis
<p>All files below are in "h5ad" format, which can be opened by the Python package AnnData (see https://anndata.readthedocs.io/en/latest). The files are organized as:</p> <p><br> 1. Single-nucleus methylcytosine sequencing (snmC-seq) of neurons from the frontal cortex of young adult mouse brains from Luo et al., 2017. The files are the preprocessed input for EpiScanpy for multiple CG imputed feature spaces, 100k base pair windows, enhancers, gene bodies, promoters and CH gene bodies.<br> - processed_enhancers_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_genebodies_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_genebodies_CH_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_promoter_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_windows_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> </p> <p>2. Single cell ATAC sequencing (scATAC-seq) from 10X Genomics, preprocessed peak count matrix of Next GEM v1.1 10k Peripheral blood mononuclear cells (PBMCs) from a healthy donor. Peak matrices of Next GEM v1.1 10k PBMCs and whole blood fresh data (GEO:GSE129785) from Satpathy et al. 2019 (Greenleaf's lab). Concatenated 5000bp matrix of Fresh cortex from adult mouse brain (P50) from 10X Genomics and CEMBA180312_3B mouse brain sample from Fang et al. 2019.<br> - atac_pbmc_10k_nextgem_fragments_macs2_peaks_outter_all_chrom.h5ad<br> - atac_pbmc_10k_nextgem_fragments_merged_peaks_for_integration_greenleaf_outter_all_chrom.h5ad<br> - raw_greenleaf_pre_integration_oct_2020.h5ad (Satpathy et al. 2019)<br> - preprocessed_10x_genomics_5k_adulte_mouse_brain_Fang_et_al_2019_CEMBA180312_3B_5kb_windows.h5ad</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>
SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data
<p>Processed data for the manuscript "" available on bioRxiv at ""</p> <p>Data is available for the following samples</p> <ol> <li>CD34+ Multiome data : 2 replicates </li> <li>T-cell depleted bone marrow Multiome data: 2 replicates </li> </ol> <p> </p> <p>The following counts and fragments files are available for each replicate </p> <ol> <li><sample>_filtered_feature_bc_matrix.h5: Feature counts from CellRanger ARC</li> <li><sample>_atac_fragments.tsv.gz: ATAC fragments file from Cellranger ARC</li> <li><sample>_atac_fragments.tsv.gz.tbi: Index files for ATAC fragments file from Cellranger ARC</li> </ol> <p> </p> <p>The following scanpy anndata objects are also available</p> <ol> <li>cd34_multiome_rna.h5ad: Anndata object with normalized data, cell type annotations and clusters for the RNA modality of CD34+ hematopoietic stem and progenitor cells </li> <li>cd34_multiome_atac.h5ad: Anndata object with peak counts, cell type annotations and clusters for the ATAC modality of of CD34+ hematopoietic stem and progenitor cells.</li> <li>cd34_multiome_rna.h5ad: Anndata object with normalized data, cell type annotations and clusters for the RNA modality of T-cell depleted bone marrow dataset.</li> <li>bm_multiome_atac.h5ad: Anndata object with peak counts, cell type annotations and clusters for the ATAC modality of T-cell depleted bone marrow dataset.</li> </ol>
Epigenome-wide analysis reveals specific DNA hypermethylation of T cells during human hematopoietic differentiation
<p>Epigenetic regulation plays an important role in cellular development and differentiation. A detailed map of the DNA methylation dynamics that occur during cell differentiation would contribute to decipher the molecular networks governing cell fate commitment. In this study we used the most recent Illumina MethylationEPIC Beadchip platform to describe the genome-wide DNA methylation changes observed throughout hematopoietic maturation by analyzing multiple hematopoietic cell types at different developmental stages.</p>
Epigenome-wide DNA Methylation and Pesticide Use in the Agricultural Lung Health Study
<p>An epigenome-wide association study of blood DNA methylation and pesticide use was conducted in adults in the Agricultural Lung Health Study. Sixteen specific pesticides were analyzed: dicamba, picloram, mesotrione, acetochlor, metolachlor, glyphosate, 2,4-Dichlorophenoxyacetic acid (2,4-D), atrazine, malathion, aldrin, chlordane, DDT, dieldrin, heptachlor, lindane, and toxaphene. 162 differentially methylated CpGs across 9 specific pesticides (acetochlor, atrazine, dicamba, glyphosate, malathion, metolachlor, mesotrione, picloram, and heptachlor.</p>
Supporting data for "Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche"
<p>This data repository contains several datasets supplementing the paper “Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche” by Fetahu, Esser-Skala, Dnyansagar et al. (2023).</p> <ul> <li>HOMER_Results.zip: detailed results of the HOMER analysis</li> <li>nblast_scopen_gene_activity_normalized_motifs_added.rds: Seurat object with scATAC-seq data</li> <li>snp_array.tgz: SNP array data</li> <li>R_data_generated.tgz: Files generated by the scRNA-seq analysis scripts in the GitHub repository associated with the publication.</li> </ul>
Genomics and Epigenomics of the Elderly Response to Pneumococcal Vaccines
ClinicalTrials.gov study NCT03104075. IPD Sharing: YES. Countries: 1. Publications: 2.
Data from: Epigenomic changes in Ostrinia moths under elevated pupal and adult temperature
Open the record for dataset details and reuse information.
Museum epigenomics: characterizing cytosine methylation in historic museum specimens
<p>Museum genomics has transformed the field of collections-based research, opening up a range of new research directions for paleontological specimens as well as natural history specimens collected over the past few centuries. Recent work demonstrates that it is possible to characterize epigenetic markers such as DNA methylation in well preserved ancient tissues. This approach has not yet been tested in traditionally prepared natural history specimens such as dried bones and skins, the most common specimen types in vertebrate collections. In this study, we developed and tested methods to characterize cytosine methylation in dried skulls up to 76 years old. Using a combination of ddRAD and bisulphite treatment, we characterized patterns of cytosine methylation in two species of deer mouse (Peromyscus spp.) collected in the same region in Michigan in 1940, 2003, and 2013–2016. We successfully estimated methylation in specimens of all age groups, although older specimens yielded less data and showed greater interindividual variation in data yield than newer specimens. Global methylation estimates were reduced in the oldest specimens (76 years old) relative to the newest specimens (1–3 years old), which may reflect post-mortem hydrolytic deamination. Methylation was reduced in promoter regions relative to gene bodies and showed greater bimodality in autosomes relative to female X chromosomes, consistent with expectations for methylation in mammalian somatic cells. Our work demonstrates the utility of historic specimens for methylation analyses, as with genomic analyses; however, studies will need to accommodate the large variance in the quantity of data produced by older specimens.</p>
Supplementary data: The APOE isoforms differentially shape the transcriptomic and epigenomic landscapes of human microglia in a xenotransplantation model of Alzheimer's disease
<p>Supplementary data for: The APOE isoforms differentially shape the transcriptomic and epigenomic landscapes of human microglia in a xenotransplantation model of Alzheimer’s disease. </p> <p>Supplementary_Table1_QC: Excel sheet containing QC metrics for the RNA-seq data and the other containing QC metrics for the ATAC-seq data. </p> <p>Supplementary_Table2_DEGs: Excel sheet containing DeSeq2 differential expression analysis results for the following comparisons: APOE2 vs APOE3, APOE4 vs APOE3, APOE4 vs APOE2, APOE-KO vs APOE3. </p> <p>Supplementary_Table3_MAGMA_geneset_analysis_res: CSV file containing MAGMA gene set analysis results using the differentially expressed genes (FDR < 0.05) for the comparisons outlined in Supplementary_Table2_DEGs and three independent AD GWAS. </p> <p>Supplementary_Table4_DARs: Excel sheet containing DeSeq2 differential accessibility analysis results for the following comparisons: APOE2 vs APOE3, APOE4 vs APOE3, APOE4 vs APOE2, APOE-KO vs APOE3. </p> <p>Supplementary_Table5_sLDSC_res.csv: CSV file containing s-LDSC results using the consensus set of ATAC-seq peaks with three brain disorder GWAS (Alzheimer's disease, autism spectrum disorder, and amyotrophic lateral sclerosis). </p> <p>Supplementary_Table6_WGCNA_clusterProfiler_pathway_enrichment.csv: CSV file containing pathway enrichment results using two WGCNA-identified modules that were significantly upregulated in APOE2-expressing microglia. </p> <p>Supplementary_Table7_homer_motifEnrichment_res.xlsx: Excel sheet containing Homer motif enrichment analysis results using top 100 peaks with increased and decreased chromatin accessibility for APOE2 vs APOE3, APOE4 vs APOE3, and APOE4 vs APOE2.</p>
Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci
<p>Trans-ethnic meta-analysis results for eGFR EWAS</p> <p>Description of variables:</p> <p>CpG CPG name<br> chr Chromosome <br> pos_hg38 Position hg38 <br> Beta Effect <br> SE Standard error <br> P P value <br> totalsamplesize number of participants <br> direction direction of effect for studies <br> hetpval P value for heterogeneity <br> hetdf Degree of freedom <br> gene Gene location</p> <p>Upload of this dataset was completed by The EWAS Catalog team. The data can be queried along with hundreds of other EWAS at ewascatalog.org. To upload your EWAS summary statistics and have a zenodo DOI generated for you go to ewascatalog.org/upload. </p>
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OpenNeuro
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