Generated banner for Gtex Database
Generated logo for Gtex Database

Gtex Database

Google DeepMindOfficialPublished by Google DeepMind

  • Skill
  • Tool integration
  • Science and research
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Use when you want to retrieve quantitative RNA expression data and variant eQTL information from the GTEx (Genotype-Tissue Expression) Project across 54 non-diseased tissue sites.
— the description by Google DeepMind

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The GTEx Database skill provides an interface to the GTEx Portal API V2, enabling researchers to retrieve baseline transcriptomics data and expression Quantitative Trait Loci (eQTLs). It facilitates the mapping of gene symbols to GENCODE IDs and allows for the extraction of median TPM expression values and significant eQTL associations across 54 non-diseased adult human tissues.

What it does

  • Maps standard gene symbols to Versioned GENCODE IDs.
  • Retrieves median TPM expression levels for genes across specified tissues.
  • Identifies tissues with the highest expression levels for a given gene.
  • Fetches significant eQTLs associated with specific genes.
  • Queries significant single-tissue eQTLs within defined chromosomal regions.

When to use it

  • When you need to analyze baseline gene expression in healthy adult human tissues.
  • When investigating the relationship between genetic variants and gene expression levels.
  • When contextualizing variants within GWAS loci using eQTL data.
  • When you require programmatic access to GTEx Portal data via a CLI wrapper.

Advantages

  • Provides direct access to standardized GTEx transcriptomics and eQTL datasets.
  • Includes a CLI wrapper that manages API rate limits automatically.
  • Supports targeted queries by tissue, gene, or chromosomal region.
  • Facilitates reproducible research workflows by outputting results to JSON files.

Usage examples

“Find the top 5 tissues where the GATA4 gene is most highly expressed.”
The agent resolves the GATA4 gene symbol to its GENCODE ID and then queries the GTEx database to return the top 5 tissues by median TPM.
“Get all significant eQTLs for the TNF gene in Whole Blood.”
The agent retrieves the relevant GENCODE ID for TNF and fetches the associated eQTL data specifically for the Whole Blood tissue site.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google-deepmind/gtex-database/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gtex-database.zip -d .agents/skills

Documentation by Google DeepMind

GTEx Database Integration

This skill retrieves transcriptomics data (RNA expression baselines) and expression Quantitative Trait Loci (eQTLs) from the GTEx Portal API V2. It provides access to median TPM (Transcripts Per Million) values for genes and significant eQTLs for variants across 54 human tissue sites.

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.

  2. User Notification: If .licenses/gtex_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://gtexportal.org/home/license and https://gtexportal.org/home/documentationPage#gtexApi, then (2) create the file recording the notification text and timestamp.

When to Use

Use this skill when you need to:

  • Map a gene symbol to its Versioned GENCODE ID.

  • Retrieve the baseline median expression level (in TPM) of a gene across various tissues.

  • Find the top tissues where a particular gene is most highly expressed.

  • Fetch significant single-tissue eQTLs for a variant or within a chromosomal window.

  • Get all significant eQTLs associated with a specific gene.

  • Contextualise a variant within GWAS loci using eQTL data.

Do NOT use when you need to:

  • Query for protein-level expression or post-translational modifications (PTMs). GTEx only measures mRNA abundance.

  • Query gene expression in diseased tissues (e.g., tumor samples, cirrhosis). GTEx is a baseline atlas of normal, non-diseased tissues.

  • Query embryonic or fetal gene expression. GTEx donors are adults only.

Core Rules

CRITICAL: You MUST respect GTEx Portal API Terms of Use.

  • Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.

  • Limit requests to maximum 250 items per page where applicable.

  • Notification: If this skill is used, ensure this is mentioned in the output.

Command Selection Guide

Pick the right command on the first try. Match the user's input to the correct subcommand below.

  • Map a gene symbol to GENCODE ID: resolve-gencode-id

  • Get median expression (TPM) for a gene: get-median-expression

  • Find tissues with highest expression for a gene: get-top-expressed-tissues

  • Get all eQTLs for a specific gene: get-gene-eqtls

  • Find eQTLs within a chromosomal region: get-eqtls-in-region

Quick Start

# Map the TNF gene symbol to its GENCODE ID
uv run scripts/gtex_cli.py resolve-gencode-id TNF --output /tmp/tnf_id.json

# Get median expression of a gene by GENCODE ID
uv run scripts/gtex_cli.py get-median-expression ENSG00000232810.2 --output /tmp/tnf_expr.json

All subcommands write JSON to disk. Always save output in the /tmp/ directory. The default output file is /tmp/gtex_output.json if --output is not specified.

Commands

1. resolve-gencode-id — Gene Symbol → GENCODE ID

Maps a standard gene symbol (e.g., "JUN", "TNF") to its Versioned GENCODE ID. This ID is required for all other expression and eQTL calls.

uv run scripts/gtex_cli.py resolve-gencode-id TNF --output /tmp/tnf_id.json

Arguments:

  • gene_symbol (positional): The standard gene symbol (e.g., "TNF").

  • --output: Output file path (default: /tmp/gtex_output.json).

2. get-median-expression — Get Median Expression (TPM)

Retrieves the median TPM for a gene across all 54 GTEx tissue sites or specified tissues.

uv run scripts/gtex_cli.py get-median-expression ENSG00000232810.2 \
  --tissues "Whole Blood,Spleen" --output /tmp/expr.json

Arguments:

  • gencode_id (positional): The Versioned GENCODE ID.

  • --tissues: Comma-separated list of tissue IDs (optional, defaults to all 54 tissues).

  • --output: Output file path (default: /tmp/gtex_output.json).

3. get-top-expressed-tissues — Get Top Expressed Tissues

Returns the n tissues with the highest median expression for the target gene.

uv run scripts/gtex_cli.py get-top-expressed-tissues ENSG00000232810.2 \
  --n 5 --output /tmp/top_tissues.json

Arguments:

  • gencode_id (positional): The Versioned GENCODE ID.

  • --n: Number of top tissues to return (default: 5).

  • --output: Output file path.

4. get-gene-eqtls — Get All eQTLs for a Gene

Returns every significant eQTL associated with the gene across specified tissues.

uv run scripts/gtex_cli.py get-gene-eqtls ENSG00000232810.2 \
  --tissues "Whole Blood" --output /tmp/eqtls.json

Arguments:

  • gencode_id (positional): The Versioned GENCODE ID.

  • --tissues: Comma-separated list of tissue IDs (optional, defaults to all).

  • --output: Output file path.

5. get-eqtls-in-region — Get eQTLs in Chromosomal Region

Returns all significant single-tissue eQTLs within a chromosomal window (up to 8Mb).

uv run scripts/gtex_cli.py get-eqtls-in-region chr17 7000000 7100000 "Esophagus - Muscularis" \
  --output /tmp/region_eqtls.json

Arguments:

  • chromosome (positional): Chromosome name (e.g., chr17).

  • start (positional): Start position.

  • end (positional): End position (max 8Mb from start).

  • tissue_id (positional): The target tissue ID.

  • --output: Output file path.

Typical Workflows

Identify highest expressing tissues for a gene
# Step 1: Map symbol to GENCODE ID
uv run scripts/gtex_cli.py resolve-gencode-id GATA4 --output /tmp/gata4_id.json

# Step 2: Query for top tissues using the resolved ID
uv run scripts/gtex_cli.py get-top-expressed-tissues <gencode_id> --n 5 \
  --output /tmp/gata4_top.json

Files

references/citation.bib

@ARTICLE{Consortium2020,
  ABSTRACT = {The Genotype-Tissue Expression (GTEx) project dissects how genetic variation affects gene expression and splicing. Some human genetic variants affect the amount of RNA produced and the splicing of gene transcripts, crucial steps in development and maintaining a healthy individual. However, some of these changes only occur in a small number of tissues within the body. The Genotype-Tissue Expression (GTEx) project has been expanded over time, and, looking at the final data in version 8, Aguet et al. present a deep characterization of genetic associations and gene expression and splicing in 838 individuals over 49 tissues (see the Perspective by Wilson). This large study was able to characterize the details underlying many aspects of gene expression and provides a resource with which to better understand the fundamental molecular mechanisms of how genetic variants affect gene regulation and complex traits in humans. Science, this issue p. 1318; see also p. 1298 The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.},
  AUTHOR = {{The GTEx Consortium} and Aguet, François and Anand, Shankara and Ardlie, Kristin G. and Gabriel, Stacey and Getz, Gad A. and Graubert, Aaron and Hadley, Kane and Handsaker, Robert E. and Huang, Katherine H. and Kashin, Seva and Li, Xiao and MacArthur, Daniel G. and Meier, Samuel R. and Nedzel, Jared L. and Nguyen, Duyen T. and Segrè, Ayellet V. and Todres, Ellen and Balliu, Brunilda and Barbeira, Alvaro N. and Battle, Alexis and Bonazzola, Rodrigo and Brown, Andrew and Brown, Christopher D. and Castel, Stephane E. and Conrad, Donald F. and Cotter, Daniel J. and Cox, Nancy and Das, Sayantan and de Goede, Olivia M. and Dermitzakis, Emmanouil T. and Einson, Jonah and Engelhardt, Barbara E. and Eskin, Eleazar and Eulalio, Tiffany Y. and Ferraro, Nicole M. and Flynn, Elise D. and Fresard, Laure and Gamazon, Eric R. and Garrido-Martín, Diego and Gay, Nicole R. and Gloudemans, Michael J. and Guigó, Roderic and Hame, Andrew R. and He, Yuan and Hoffman, Paul J. and Hormozdiari, Farhad and Hou, Lei and Im, Hae Kyung and Jo, Brian and Kasela, Silva and Kellis, Manolis and Kim-Hellmuth, Sarah and Kwong, Alan and Lappalainen, Tuuli and Li, Xin and Liang, Yanyu and Mangul, Serghei and Mohammadi, Pejman and Montgomery, Stephen B. and Muñoz-Aguirre, Manuel and Nachun, Daniel C. and Nobel, Andrew B. and Oliva, Meritxell and Park, YoSon and Park, Yongjin and Parsana, Princy and Rao, Abhiram S. and Reverter, Ferran and Rouhana, John M. and Sabatti, Chiara and Saha, Ashis and Stephens, Matthew and Stranger, Barbara E. and Strober, Benjamin J. and Teran, Nicole A. and Viñuela, Ana and Wang, Gao and Wen, Xiaoquan and Wright, Fred and Wucher, Valentin and Zou, Yuxin and Ferreira, Pedro G. and Li, Gen and Melé, Marta and Yeger-Lotem, Esti and Barcus, Mary E. and Bradbury, Debra and Krubit, Tanya and McLean, Jeffrey A. and Qi, Liqun and Robinson, Karna and Roche, Nancy V. and Smith, Anna M. and Sobin, Leslie and Tabor, David E. and Undale, Anita and Bridge, Jason and Brigham, Lori E. and Foster, Barbara A. and Gillard, Bryan M. and Hasz, Richard and Hunter, Marcus and Johns, Christopher and Johnson, Mark and Karasik, Ellen and Kopen, Gene and Leinweber, William F. and McDonald, Alisa and Moser, Michael T. and Myer, Kevin and Ramsey, Kimberley D. and Roe, Brian and Shad, Saboor and Thomas, Jeffrey A. and Walters, Gary and Washington, Michael and Wheeler, Joseph and Jewell, Scott D. and Rohrer, Daniel C. and Valley, Dana R. and Davis, David A. and Mash, Deborah C. and Branton, Philip A. and Barker, Laura K. and Gardiner, Heather M. and Mosavel, Maghboeba and Siminoff, Laura A. and Flicek, Paul and Haeussler, Maximilian and Juettemann, Thomas and Kent, W. James and Lee, Christopher M. and Powell, Conner C. and Rosenbloom, Kate R. and Ruffier, Magali and Sheppard, Dan and Taylor, Kieron and Trevanion, Stephen J. and Zerbino, Daniel R. and Abell, Nathan S. and Akey, Joshua and Chen, Lin and Demanelis, Kathryn and Doherty, Jennifer A. and Feinberg, Andrew P. and Hansen, Kasper D. and Hickey, Peter F. and Jasmine, Farzana and Jiang, Lihua and Kaul, Rajinder and Kibriya, Muhammad G. and Li, Jin Billy and Li, Qin and Lin, Shin and Linder, Sandra E. and Pierce, Brandon L. and Rizzardi, Lindsay F. and Skol, Andrew D. and Smith, Kevin S. and Snyder, Michael and Stamatoyannopoulos, John and Tang, Hua and Wang, Meng and Carithers, Latarsha J. and Guan, Ping and Koester, Susan E. and Little, A. Roger and Moore, Helen M. and Nierras, Concepcion R. and Rao, Abhi K. and Vaught, Jimmie B. and Volpi, Simona},
  URL = {https://www.science.org/doi/abs/10.1126/science.aaz1776},
  DATE = {2020},
  DOI = {10.1126/science.aaz1776},
  EPRINT = {https://www.science.org/doi/pdf/10.1126/science.aaz1776},
  JOURNALTITLE = {Science},
  NUMBER = {6509},
  PAGES = {1318--1330},
  TITLE = {The {GTEx Consortium} atlas of genetic regulatory effects across human tissues},
  VOLUME = {369},
}

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Google DeepMind
Publisher
Google DeepMind (Official)
Source
google-deepmind/science-skills · skills/gtex_database · commit 4de6786
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Google DeepMind.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download gtex-database.zip