Download phenotype file gtex

These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program.

Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes. The Genotype-Tissue Expression GTEx project, sponsored by the NIH Common Fund, was established to study the correlation between human genetic variation and tissue-specific gene expression in non-diseased individuals.

A significant challenge was the collection of high-quality biospecimens for extensive genomic analyses. Here we describe how a successful infrastructure for biospecimen procurement was developed and implemented by multiple research partners to support the prospective collection, annotation, and distribution of blood, tissues, and cell lines for the GTEx project.

Other research projects can follow this model and form beneficial partnerships with rapid autopsy and organ procurement organizations to collect high quality biospecimens and associated clinical data for genomic studies. Biospecimens, clinical and genomic data, and Standard Operating Procedures guiding biospecimen collection for the GTEx project are available to the research community.

Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes.

Here we describe the Genotype-Tissue Expression GTEx project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.

It also features an image library of the tissue samples, and a form to request tissue samples. However, due to the nature of our donor consent agreement, raw data and attributes which might be used to identify the donors, such as raw sequencing data or variant calls, are not publicly available on the GTEx Portal.

Once you are approved for accessing the raw GTEx data, you can either analyze the protected data within the cloud using the Terra Platform within this workspace or you can download the protected data to your home institutions for free using the instructions provided below.

For many analyses, it will be substantially easier and more efficient to perform your analysis within Terra as Terra provides the capabilities for large scale batch processing and interactive analysis over thousands of samples.

Please note that you should not attempt to export the protected data from the Terra workspace, as this will incur egress fees as the data are exported from the cloud environment. Instead, please follow the directions below to download the data free of charge from the Gen3 platform.

If you elect to download the protected data, it is your responsibility to maintain data security and privacy within your institutional servers. Add a comment. Active Oldest Votes. See below 2 links. Improve this answer. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. RNA-Seq is very noisy data. Finally, if you want a specific tissue, you have to find samples of that tissue. I have found that subsetting the data worked to get data from one tissue only.

The gene names start with ENSGxxxx. I found out that they are different transcripts, sometimes of the same gene, so I suppose there are no real duplicates. Add a comment. Active Oldest Votes. So it is an integer and the values are not directly comparable between samples due to differences in sequencing depth. TPM stands transcripts per million. It is the normalized gene expression level.

Basically, you first normalize read counts by gene length and then normalize the library size to 1e6 in all libraries. As for which one should you use, it depends on what analysis you want to do and what tool you are going to use. I have not noticed that there are duplicated genes in those files, can you show an example? I do not think you can download expression from one tissue, but you can always subset.