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Dataset results
99 results for “GTEx”
Full summary statistics of mixQTL for GTEx v8 Esophagus_Mucosa
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Brain_Amygdala
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Minor_Salivary_Gland
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
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.
Full summary statistics of mixQTL for GTEx v8 Adipose_Visceral_Omentum
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Artery_Aorta
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Nerve_Tibial
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Pancreas
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Adrenal_Gland
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Brain_Cerebellar_Hemisphere
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Cells_EBV-transformed_lymphocytes
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Brain_Spinal_cord_cervical_c-1
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Skin_Sun_Exposed_Lower_leg
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Thyroid
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Stomach
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Whole_Blood
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Uterus
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>
CTIMP models on GTEx v8
<p>Cross-tissue imputation models for gene expression trained on GTEx v8 European individuals. See detailed description in https://doi.org/10.1002/gepi.22346</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>
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.