Description

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.

Display Conventions

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. Tissue colors were assigned to conform to the GTEx Consortium publication conventions.
     
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. The Squish display mode draws a rectangle for each gene, colored to indicate the tissue with highest expression level if it contributes more than 10% to the overall expression (and colored black if no tissue predominates). In Dense mode, the darkness of the grayscale rectangle displayed for the gene reflects the total median expression level across all tissues.

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.

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.

The track configuration page provides controls to limit the genes and tissues displayed, and to select raw or log transformed expression level display.

Methods

Tissue samples were obtained using the GTEx standard operating procedures for informed consent and tissue collection, in conjunction with the National Cancer Institute Biorepositories and Biospecimen. All tissue specimens were reviewed by pathologists to characterize and verify organ source. Images from stained tissue samples can be viewed via the NCI histopathology viewer. The Qiagen PAXgene non-formalin tissue preservation product was used to stabilize tissue specimens without cross-linking biomolecules.

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.

Subject and Sample Characteristics

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.

Data Access

The raw data for the GTEx Gene expression track can be accessed interactively through the Table Browser or Data Integrator. Metadata can be found in the connected tables below.

• gtexGeneModel describes the gene names and coordinates in genePred format.
• hgFixed.gtexTissue lists each of the 53 tissues in alphabetical order, corresponding to the comma separated expression values in gtexGene.
• hgFixed.gtexSampleData has RPKM expression scores for each individual gene-sample data point, connected to gtexSample.
• hgFixed.gtexSample contains metadata about sample time, collection site, and tissue, connected to the donor field in the gtexDonor table.
• hgFixed.gtexDonor has anonymized information on the tissue donor.

For automated analysis and downloads, the track data files can be downloaded from our downloads server or the JSON API. Individual regions or the whole genome annotation can be accessed as text using our utility bigBedToBed. Instructions for downloading the utility can be found here. That utility can also be used to obtain features within a given range, e.g. bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/gtex/gtexTranscExpr.bb -chrom=chr21 -start=0 -end=100000000 stdout

Data can also be obtained directly from GTEx at the following link: https://gtexportal.org/home/datasets

Credits

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.

References

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: PMC4547472

DeLuca 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