The NIH Genotype-Tissue Expression (GTEX) project was created to establish a sample and data resource for studies on the relationship between genetic variation and gene expression in multiple human tissues. This track shows median gene expression levels in 51 tissues and 2 cell lines, based on RNA-seq data from the GTEx midpoint milestone data release (V6, October 2015). This release is based on data from 8555 tissue samples obtained from 570 adult post-mortem individuals.
In Full and Pack display modes, expression for each gene is represented by a colored bargraph,
where the height of each bar represents the median expression level across all samples for a
tissue, and the bar color indicates the tissue.
The bargraph display has the same width and tissue order for all genes.
Mouse hover over a bar will show the tissue and median expression level.
Tissue colors were assigned to conform to the GTEx Consortium publication conventions.
The GTEx transcript model used to quantify expression level is displayed below the graph, colored to indicate the transcript class (coding, noncoding, pseudogene, problem), following GENCODE conventions.
The track configuration page provides a filter to limit the tissues displayed, choice of raw or log transformed expression level display, and an option to segment the samples by gender and display a difference graph.
Click-through on a graph displays a boxplot of expression level quartiles with outliers, per tissue, along with a link to the corresponding gene page on the GTEX Portal.RNA-seq was performed by the GTEx Laboratory, Data Analysis and Coordinating Center (LDACC) at the Broad Institute. The Illumina TruSeq protocol was used to create an unstranded polyA+ library sequenced on the Illumina HiSeq 2000 platform to produce 76-bp paired end reads at a depth averaging 50M aligned reads per sample. Sequence reads were aligned to the hg19/GRCh37 human genome using Tophat v1.4.1 assisted by the GENCODE v19 transcriptome definition. Gene annotations were produced by taking the union of the GENCODE exons for each gene. Gene expression levels in RPKM were called via the RNA-SeQC tool, after filtering for unique mapping, proper pairing, and exon overlap. For further method details, see the GTEX Portal Documentation page.
UCSC obtained the gene-level expression files, gene annotations and sample metadata from the GTEX Portal Download page. Median expression level in RPKM was computed per gene/per tissue.
The scientific goal of the GTEx project required that the donors and their biospecimen present with no evidence of disease. The tissue types collected were chosen based on their clinical significance, logistical feasibility and their relevance to the scientific goal of the project and the research community. Postmortem samples were collected from non-diseased donors with ages ranging from 20 to 79. 34.4% of donors were female and 65.6% male.
Additional summary plots of GTEx sample characteristics are available at the GTEx Portal Tissue Summary page.
Statistical analysis and data interpretation was performed by The GTEx Consortium Analysis Working Group. Data was provided by the GTEx LDACC at The Broad Institute of MIT and Harvard.
GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013 Jun;45(6):580-5. PMID: 23715323; PMC: PMC4010069
Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, Compton CC, DeLuca DS, Peter-Demchok J, Gelfand ET et al. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project. Biopreserv Biobank. 2015 Oct;13(5):311-9. PMID: 26484571; PMC: PMC4675181
Melé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ et al. Human genomics. The human transcriptome across tissues and individuals. Science. 2015 May 8;348(6235):660-5. PMID: 25954002; PMC: PMC4547472DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, Reich M, Winckler W, Getz G. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012 Jun 1;28(11):1530-2. PMID: 22539670; PMC: PMC3356847