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11 results for “GTEx tissues”
Data used in ECLIPSER methods paper and GTEx snRNA-seq cross-tissue reference map analysis
<p>The tables were used in the papers: Rouhana*, Wang* <em>et al.,</em> ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci, bioRxiv 2021, doi: https://doi.org/10.1101/2021.11.24.469720; and Eraslan <em>et al.,</em> Single-nucleus cross-tissue molecular reference maps to decipher disease gene function, bioRxiv 2021, doi: https://doi.org/10.1101/2021.07.19.452954. '<a href="https://zenodo.org/api/files/1f8d48d0-6bf7-4bec-b6ef-5c7a9ead8079/GTEx_v8_HG38_all_variants.tsv.gz">GTEx_v8_HG38_all_variants.tsv.gz</a>' is an input file for running GWASvar2gene on GTEx v8 eQTLs and sQTLs, and all other files are input files for ECLIPSER.</p>
Full summary statistics of mixQTL for GTEx v8 Breast_Mammary_Tissue
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Gene expression and splicing counts from 49 tissues from GTEx v6p genome build hg19 - non-strand specific
<p><strong>Dataset description:</strong></p> <p>49 folders, each corresponding to one tissue from GTEx v6p and containing the following files:</p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p>The gene counts were originated using the GTF file from <a href="http://www.gencodegenes.org/human/release_29lift37.html">release 29 of GENCODE</a>, and the split and non-split counts contain only the annotated junctions from the same release. Statistics are reported only for GENCODE-annotated introns and splice sites, in compliance with the regulations of the GTEx consortium. For a description of the samples, methods, and protocols, see the GTEx publication specified below.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the <a href="https://github.com/gagneurlab/drop">Detection of RNA outliers Pipeline (DROP) </a> can be used to compute gene expression and splicing outliers.<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode29<br> <strong>Strand specific: </strong>FALSE<br> <strong>Paired end: </strong>TRUE<br> <strong>Protocol: </strong>poly(A) enrichment</p> <p><strong>Contact:</strong> Vicente A. Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de</p> <p><strong>Citation:</strong> Write the following in the "Data availability" section of the manuscript or similar replacing the three citations by the ones from the References section below:</p> <blockquote> <p><strong>The count matrices for the GTEx samples <cite GTEx publication, see below> were downloaded from Zenodo (doi: 10.5281/zenodo.5596755) and were generated through DROP <cite DROP, see below> using the release 29 of the GENCODE annotation <cite GENCODE, see below>. </strong></p> </blockquote> <p>Also, write the following in the Acknowledgements section:<br> </p> <blockquote> <p><strong>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The raw data used for the analyses described in this manuscript were obtained from the GTEx Portal on June 12, 2017, under accession number dbGaP phs00424.v6.p1.</strong></p> </blockquote> <p><br> </p>
Gene expression and splicing counts from 49 tissues from GTEx v8 genome build hg38 - non-strand specific
<p><strong>Dataset description:</strong></p> <p>49 folders, each corresponding to one tissue from GTEx v8 and containing the following files:</p> <ol> <li> <p>geneCounts: gene-level counts </p> </li> <li> <p>k_j: split counts spanning from one exon to another.</p> </li> <li> <p>k_theta: non-split counts covering a splice site</p> </li> <li> <p>n_psi3: total split counts from a given acceptor site</p> </li> <li> <p>n_psi5: total split counts from a given donor site</p> </li> <li> <p>n_theta: total split and non-split counts for a given splice site</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p>The gene counts were originated using the GTF file from <a href="https://www.gencodegenes.org/human/release_29.html">release 29 of GENCODE</a>, and the split and non-split counts contain only the annotated junctions from the same release. Statistics are reported only for GENCODE-annotated introns and splice sites, in compliance with the regulations of the GTEx consortium. For a description of the samples, methods, and protocols, see the GTEx publication specified below.</p> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the <a href="https://github.com/gagneurlab/drop">Detection of RNA outliers Pipeline (DROP) </a> can be used to compute gene expression and splicing outliers.<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg38<br> <strong>Gene annotation:</strong> gencode29<br> <strong>Strand specific: </strong>FALSE<br> <strong>Paired end: </strong>TRUE<br> <strong>Protocol: </strong>poly(A) enrichment</p> <p><strong>Contact:</strong> Vicente A. Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de</p> <p><strong>Citation:</strong> Write the following in the "Data availability" section of the manuscript or similar replacing the three citations by the ones from the References section below:</p> <blockquote> <p><strong>The count matrices for the GTEx samples <cite GTEx publication, see below> were downloaded from Zenodo (doi: 10.5281/zenodo.6078397) and were generated through DROP <cite DROP, see below> using the release 29 of the GENCODE annotation <cite GENCODE, see below>. </strong></p> </blockquote> <p>Also, write the following in the Acknowledgements section:<br> </p> <blockquote> <p><strong>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The raw data used for the analyses described in this manuscript were obtained from the GTEx Portal on June 12, 2017, under accession number dbGaP phs000424.v8.p2.</strong></p> </blockquote> <p> </p>
isoTWAS models using 48 GTEx tissues and PsychENCODE data (07.04.22)
<p>isoTWAS models for 48 GTEx tissues, adult frontal cortex tissue from the CommonMind Consortium (subset of PsychENCODE project; Gandal et al 2018, <em>Science</em>), and fetal frontal cortext from Walker et al 2019, <em>Cell</em>.</p> <p>Each folder corresponds to a separate tissue and contains 1 .tsv.gz file per gene that contains the isoTWAS model. Refer to https://bhattacharya-a-bt.github.io/isotwas/ on how to use these models.</p>
aFCn for independent cis-eQTLs across 49 tissues in the GTEx v8 release
<p>The aFC-n model is the multi-variant generalization of the aFC approach (<a href="https://github.com/secastel/aFC">https://github.com/secastel/aFC</a>), to estimate the <i>cis</i>-regulatory effect size in genes associated with multiple conditionally independent eQTLs. Notably, aFC-n is the first method that allows for predicting genetically regulated gene expression in a haplotype-specific fashion, paving the way for a future class of genome association studies that can systematically incorporate allele dosage effects.</p><p>This dataset is generated using aFC-n software package (<a href="https://doi.org/10.5281/zenodo.8412460">10.5281/zenodo.8412460</a>) to calculate effect sizes for ~half a million independent <i>cis</i>-eQTLs across 49 tissues in the GTEx v8 release as described in the <a href="https://www.biorxiv.org/content/10.1101/2022.01.28.478116v1">manuscript</a>.</p><p>GTEx_v8_aFCn contains:</p><ul><li>gene_id : Ensembl gene ID</li><li>variant_id : eQTL ID associated to the gene</li><li>log2_aFC : The log2 of eQTL effect sizes measured as allelic Fold Change (aFC).</li><li>log2_aFC_min_95_interv : Lower bound 95% confidence interval</li><li>log2_aFC_plus_95_interv: Upper bound 95% confidence interval</li></ul><p>GTEx_v8<i>_</i>aFCn<i>_</i>combined contains:</p><ul><li>gene_id : Ensembl gene ID</li><li>variant_id : eQTL ID associated to the gene</li><li>rest of the columns : The log2 of eQTL effect sizes measured as allelic Fold Change (aFC) in the tissue specified by the column label capped at ±log2(100) <ul><li>nan = The variant is not an eQTL for the tissue / the effect size is not calculated for variants on chrX</li></ul></li></ul><p> </p>
Nucleus feature profiles from GTEx histological images across 12 tissues
Open the record for dataset details and reuse information.
GTEx: DICOM converted whole slide hematoxylin and eosin stained images from the Genotype-Tissue Expression (GTEx) Project
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=gtex" target="_blank" rel="noopener">GTEx</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> <p>The<a href="https://commonfund.nih.gov/GTEx"> Genotype-Tissue Expression (GTEx) Project</a> established a data resource and tissue bank to study the relationship between genetic variants and gene expression in multiple human tissues and across individuals. The project included contributions from numerous groups with diverse expertise in biospecimen collection and processing, pathology review, molecular analysis, and data management. The contributors are collectively called the GTEx Consortium.</p> <p>GTEx collected a total of 26,468 unique tissue samples from 50+ different tissue types, from 956 healthy postmortem donors. The standardized biospecimen collection and analysis practices applied during the study served to minimize preanalytical variability associated with specimen-related factors and their potential impact on analytic endpoints. Each GTEx tissue was divided into two tissue blocks, one for histology and one for molecular analysis; both tissue blocks were preserved in PAXgene Tissue Fixative (Qiagen) solution for 6 to 24 hours, followed by PAXgene Tissue Stabilizer (Qiagen) as specified in the project-specific<a href="https://biospecimens.cancer.gov/resources/sops/library.asp"> standard operating procedures</a>. Tissue blocks were processed and embedded in paraffin at the GTEx central repository at the Van Andel Institute (MI) and hematoxylin and eosin–stained slides were generated from all GTEx donors. Digitally scanned whole slide images of PAXgene-fixed/stabilized, paraffin-embedded tissue sections were created using Aperio Scanscope software (Leica Biosystems). The digital images were then reviewed and annotated by one of four board-certified pathologists assigned to the GTEx study. There are a total of 25,503 digital histology images in the GTEx collection.</p> <p>GTEx was supported by the NIH Common Fund (2010 – 2019). Additional resources include the<a href="https://gtexportal.org/home/biobank"> GTEx Biobank</a>, the<a href="https://gtexportal.org/home/"> GTEx Portal</a>, and the full dataset at<a href="https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v9.p2"> dbGaP</a> (accession number phs000424).</p> <p>Please refer to the listed GTEx publications below for more details [2-7]. </p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>gtex-idc_v19-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>gtex-idc_v19-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>gtex-idc_v19-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <div>Please acknowledge the GTEx Consortium in any published work that includes the images. A sample statement for the acknowledgment of the Genotype-Tissue Expression (GTEx) Project dataset(s) follows.</div> <p>The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (<a href="http://commonfund.nih.gov/GTEx" target="_blank" rel="noopener">commonfund.nih.gov/GTEx</a>). Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI/Leidos Biomedical Research, Inc. subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to the Broad Institute of MIT and Harvard. Biorepository operations were funded through a Leidos Biomedical Research, Inc. subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by Leidos Biomedical Research, Inc. (HHSN261200800001E). The Brain Bank was supported with supplements to University of Miami grant DA006227. Statistical Methods development grants were made to the University of Geneva (MH090941& MH101814), the University of Chicago (MH090951, MH090937, MH101825, & MH101820), the University of North Carolina - Chapel Hill (MH090936), North Carolina State University (MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University (MH101810), and to the University of Pennsylvania (MH101822).</p> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. <em>Radiographics</em> <strong>43,</strong> (2023).</p> <p>[2] Sobin, L., Barcus, M., Branton, P. A., Engel, K. B., Keen, J., Tabor, D., Ardlie, K. G., Greytak, S. R., Roche, N., Luke, B., Vaught, J., Guan, P. & Moore, H. M. Histologic and quality assessment of genotype-Tissue Expression (GTEx) research samples: A large postmortem tissue collection. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0467-OA">10.5858/arpa.2023-0467-OA</a></p> <p>[3] GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).</p> <p>[4] GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).</p> <p>[5] GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).</p> <p>[6] Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., Compton, C. C., DeLuca, D. S., Peter-Demchok, J., Gelfand, E. T., Guan, P., Korzeniewski, G. E., Lockhart, N. C., Rabiner, C. A., Rao, A. K., Robinson, K. L., Roche, N. V., Sawyer, S. J., Segrè, A. V., Shive, C. E., Smith, A. M., Sobin, L. H., Undale, A. H., Valentino, K. M., Vaught, J., Young, T. R., Moore, H. M. & GTEx Consortium. A novel approach to high-quality postmortem tissue procurement: The GTEx project. Biopreserv. Biobank. 13, 311–319 (2015).</p> <p>[7] Branton, P. A., Sobin, L., Barcus, M., Engel, K. B., Greytak, S. R., Guan, P., Vaught, J. & Moore, H. M. Notable histologic findings in a ‘normal’ cohort: The National Institutes of Health Genotype-Tissue Expression (GTEx) project. Arch. Pathol. Lab. Med. (2024). doi:<a href="http://dx.doi.org/10.5858/arpa.2023-0468-OA">10.5858/arpa.2023-0468-OA</a></p>
data for Multi-tissue H3K27ac profiling of GTEx samples links epigenomic variation to disease
<p>processed data for "Multi-tissue H3K27ac profiling of GTEx samples links epigenomic variation to disease"</p>
Methylation data from nine tissues from GTEx samples profiled with Infinium HumanMethylationEPIC BeadChip
GEO Series GSE213478. Homo sapiens. 987 samples. Type: Methylation profiling by array.
Broad Institute L1000 Connectivity Map and RNAseq gene expression profiles on tissue samples from the GTEx project
GEO Series GSE92743. Homo sapiens. 0 samples. Type: Expression profiling by array.
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