version: "1.0.1" name: gtex-expression description: "Guide for integrating GTEx tissue expression data with ENCODE regulatory elements. Use when users need to check if a gene is expressed in a tissue, correlate regulatory elements with expression, or validate ENCODE findings against GTEx. Trigger on: GTEx, tissue expression, gene expression levels, expression atlas, eQTL, tissue-specific expression, TPM values."

Integrating GTEx Tissue Expression with ENCODE Regulatory Data

Use GTEx gene expression across 54 human tissues to validate ENCODE regulatory element activity, establish enhancer-gene links, and provide tissue-specific expression context for functional genomics findings.

Scientific Rationale

The question: "Is the gene near my ENCODE regulatory element actually expressed in the tissue where the element is active?"

ENCODE catalogs where regulatory elements exist (enhancers, promoters, insulators) but does not directly measure gene expression across a broad tissue panel. GTEx (Genotype-Tissue Expression) fills this gap by providing RNA-seq-based gene expression measurements across 54 human tissues from ~1,000 post-mortem donors. Integrating the two answers a fundamental question: does the regulatory landscape match the transcriptional output?

An active enhancer (H3K27ac+, ATAC-seq+) near a gene in pancreas tissue is much more meaningful if GTEx confirms the gene is highly expressed in pancreas. Conversely, an ENCODE enhancer near a gene with zero expression in the relevant tissue suggests the enhancer regulates a different gene, or acts in a cell-type subpopulation not captured by bulk GTEx.

What GTEx Provides

• Median gene expression (TPM) across 54 human tissues from ~1,000 donors
• Transcript-level expression for isoform analysis
• eQTLs — variants associated with gene expression in specific tissues (cis and trans)
• sQTLs — variants associated with alternative splicing
• Single-nucleus RNA-seq for selected tissues (GTEx v8+)
• Allele-specific expression data

Why Integrate with ENCODE

Key Literature

• GTEx Consortium 2020 "The GTEx Consortium atlas of genetic regulatory effects across human tissues" (Science, ~4,000 citations). The flagship publication describing the v8 release with 17,382 samples across 54 tissues from 948 donors. Identified cis-eQTLs for 95% of genes, tissue-sharing patterns, and cell-type interaction eQTLs. DOI: 10.1126/science.aaz1776
• Aguet et al. 2017 "Genetic effects on gene expression across human tissues" (Nature, ~3,000 citations). The v6p analysis establishing the multi-tissue eQTL framework, demonstrating widespread tissue-specific genetic regulation. DOI: 10.1038/nature24277
• ENCODE Project Consortium 2020 (Nature, ~1,656 citations). Registry of 926,535 human cCREs. Provides the regulatory element catalog to cross-reference with GTEx expression. DOI: 10.1038/s41586-020-2493-4
• Nasser et al. 2021 (Nature, ~468 citations). ABC model linking enhancers to genes using ENCODE data. GTEx expression validates ABC-predicted enhancer-gene pairs. DOI: 10.1038/s41586-021-03446-x

When to Use This Skill

GTEx REST API Reference

Base URL: https://gtexportal.org/api/v2

No authentication required. Responses are JSON.

Key Endpoints

Dataset IDs

• gtex_v8 — Current release (54 tissues, 948 donors, 17,382 samples)

Step 1: Identify the Gene of Interest from ENCODE Data

The starting point is typically an ENCODE finding — an active regulatory element near a gene:

# Find enhancers in pancreas
encode_search_experiments(assay_title="Histone ChIP-seq", target="H3K27ac", organ="pancreas")
 
# Get peak files
encode_list_files(
    experiment_accession="ENCSR...",
    file_format="bed",
    output_type="IDR thresholded peaks",
    assembly="GRCh38"
)

From the peak file, identify the nearest gene(s) to the enhancer. You will need the Ensembl gene ID (ENSG...) for GTEx queries.

Step 2: Query GTEx for Gene Expression

Get median expression across all tissues

import requests
gene_id = "ENSG00000254647"  # INS (insulin)
url = f"https://gtexportal.org/api/v2/expression/geneExpression"
params = {
    "geneId": gene_id,
    "datasetId": "gtex_v8"
}
response = requests.get(url, params=params)
data = response.json()
# Each entry has: tissueSiteDetailId, median, geneSymbol, etc.
for entry in sorted(data["data"], key=lambda x: x["median"], reverse=True)[:10]:
    print(f"{entry['tissueSiteDetailId']}: {entry['median']:.1f} TPM")

Get transcript-level expression

url = "https://gtexportal.org/api/v2/expression/medianTranscriptExpression"
params = {
    "geneId": gene_id,
    "datasetId": "gtex_v8"
}
response = requests.get(url, params=params)

Step 3: Map GTEx Tissues to ENCODE Biosamples

GTEx and ENCODE use different tissue nomenclature. Key mappings:

To get the full list of GTEx tissue IDs:

url = "https://gtexportal.org/api/v2/dataset/tissueSiteDetail"
response = requests.get(url)
tissues = response.json()["data"]
for t in tissues:
    print(f"{t['tissueSiteDetailId']}: {t['tissueSiteDetail']}")

Step 4: Interpret TPM Values

Expression Thresholds

The 1 TPM threshold: Widely used as a minimum for "expressed" status. Below this, signal is difficult to distinguish from noise in bulk RNA-seq. However, genes expressed in rare cell types within a tissue (e.g., beta cells in pancreas) may show low bulk TPM but high single-cell expression.

Tissue Specificity Metric

Calculate the tau specificity index (Yanai et al. 2005):

import numpy as np
def tau_specificity(tpm_values):
    """Calculate tissue specificity (0 = ubiquitous, 1 = tissue-specific)."""
    if max(tpm_values) == 0:
        return 0
    x = np.array(tpm_values) / max(tpm_values)
    n = len(x)
    return sum(1 - x) / (n - 1)

• tau > 0.8: Highly tissue-specific (e.g., INS in pancreas, ALB in liver)
• tau 0.4 - 0.8: Moderately specific
• tau < 0.4: Broadly expressed (housekeeping genes)

Step 5: Cross-Reference ENCODE Regulatory Elements with GTEx Expression

Validation Logic

IF ENCODE shows active enhancer (H3K27ac+) near gene X in tissue T
AND GTEx shows gene X expressed (TPM > 1) in tissue T
THEN: Consistent — regulatory element likely drives expression
 
IF ENCODE shows active enhancer near gene X in tissue T
AND GTEx shows gene X NOT expressed (TPM < 1) in tissue T
THEN: Discordant — enhancer may regulate a different gene, or
      expression is cell-type-specific (below bulk detection)
 
IF GTEx shows gene X highly expressed in tissue T
AND ENCODE shows NO active enhancer near gene X in tissue T
THEN: Regulatory data may be incomplete for this tissue,
      or regulation is promoter-driven (check H3K4me3)

Using ENCODE Tools for Cross-Referencing

# Check what ENCODE regulatory data exists for the GTEx tissue
encode_get_facets(organ="pancreas")
 
# Get H3K27ac (enhancer) data
encode_search_experiments(
    assay_title="Histone ChIP-seq",
    target="H3K27ac",
    organ="pancreas",
    biosample_type="tissue"
)
 
# Get ATAC-seq (accessibility) data
encode_search_experiments(
    assay_title="ATAC-seq",
    organ="pancreas",
    biosample_type="tissue"
)

Step 6: eQTL Integration

GTEx eQTLs identify variants that regulate gene expression. These can be cross-referenced with ENCODE peaks.

Query eQTLs for a Gene

url = "https://gtexportal.org/api/v2/eqtl/singleTissueEqtl"
params = {
    "geneId": "ENSG00000254647",
    "tissueSiteDetailId": "Pancreas",
    "datasetId": "gtex_v8"
}
response = requests.get(url, params=params)
eqtls = response.json()["data"]
# Each eQTL has: variantId, pValue, nes (effect size), tissueSiteDetailId
for eqtl in eqtls[:5]:
    print(f"{eqtl['variantId']}: p={eqtl['pValue']:.2e}, NES={eqtl['nes']:.3f}")

GTEx-ENCODE eQTL Workflow

1. Get eQTLs for gene of interest in relevant tissue
2. Convert GTEx variant IDs to genomic coordinates (format: chr1_12345_A_G_b38)
3. Intersect eQTL positions with ENCODE peak files (H3K27ac, ATAC-seq, TF ChIP-seq)
4. eQTLs falling in active regulatory elements are high-confidence regulatory variants
5. Check the variant-annotation skill for full variant prioritization

Step 7: R Workflow Example

library(httr)
library(jsonlite)
 
# Query GTEx API
gene_id <- "ENSG00000254647"
url <- paste0("https://gtexportal.org/api/v2/expression/geneExpression",
              "?geneId=", gene_id, "&datasetId=gtex_v8")
response <- GET(url)
data <- fromJSON(content(response, "text", encoding = "UTF-8"))
 
# Create expression table
expr_df <- data.frame(
  tissue = data$data$tissueSiteDetailId,
  median_tpm = data$data$median,
  stringsAsFactors = FALSE
)
expr_df <- expr_df[order(-expr_df$median_tpm), ]
 
# Filter to tissues matching ENCODE organs
encode_tissues <- c("Pancreas", "Liver", "Lung", "Heart_Left_Ventricle",
                    "Brain_Cortex", "Kidney_Cortex")
matched <- expr_df[expr_df$tissue %in% encode_tissues, ]
print(matched)

Walkthrough: Tissue-Specific Expression Context for ENCODE Enhancer Targets

Goal: Use GTEx expression data to validate that genes near ENCODE enhancers are expressed in the expected tissue, providing functional context for epigenomic findings. Context: ENCODE identifies enhancers by chromatin marks, but expression data confirms the target gene is active in the same tissue.

Step 1: Identify enhancer target genes from ENCODE

Start with H3K27ac ChIP-seq peaks near gene promoters:

encode_search_experiments(assay_title="Histone ChIP-seq", organ="liver", target="H3K27ac", organism="Homo sapiens")

Expected output:

{
  "results": [
    {"accession": "ENCSR123LIV", "assay_title": "Histone ChIP-seq", "biosample_summary": "liver", "target": "H3K27ac"}
  ],
  "total": 8,
  "limit": 25,
  "offset": 0,
  "has_more": false,
  "next_offset": null
}

Step 2: Query GTEx for tissue-specific expression

Using the GTEx REST API (via skill guidance):

GET https://gtexportal.org/api/v2/expression/medianGeneExpression?gencodeId=ENSG00000134243&tissueSiteDetailId=Liver

Expected response:

{
  "medianGeneExpression": [
    {"geneSymbol": "SORT1", "tissueSiteDetailId": "Liver", "median": 45.2, "unit": "TPM"},
    {"geneSymbol": "SORT1", "tissueSiteDetailId": "Brain_Cortex", "median": 12.8, "unit": "TPM"},
    {"geneSymbol": "SORT1", "tissueSiteDetailId": "Heart_Left_Ventricle", "median": 2.1, "unit": "TPM"}
  ]
}

Interpretation: SORT1 is highly expressed in liver (45.2 TPM) — consistent with the H3K27ac enhancer mark in liver tissue. The 22x enrichment over heart confirms tissue-specific regulation.

Step 3: Validate multi-gene enhancer targets

For each gene near an ENCODE enhancer peak, check GTEx expression:

• Tissue-matched expression (TPM > 5) = concordant (enhancer is near an active gene)
• No expression in tissue (TPM < 1) = discordant (enhancer may regulate a different gene, or the gene is active in a rare cell subtype)

Step 4: Track validated experiments

encode_track_experiment(accession="ENCSR123LIV", notes="Liver H3K27ac - validated with GTEx expression for SORT1, PCSK9")

Integration with downstream skills

• Enhancer targets from peak-annotation provide gene lists to query in GTEx
• GTEx tissue specificity informs disease-research for tissue-relevant disease genes
• Compare GTEx bulk expression with cellxgene-context single-cell data for cell-type resolution
• GTEx eQTL data connects to gwas-catalog trait-associated variants

Code Examples

1. Find ENCODE experiments matching a GTEx tissue

encode_search_experiments(assay_title="Histone ChIP-seq", organ="brain", target="H3K27ac", organism="Homo sapiens")

Expected output:

{
  "results": [
    {"accession": "ENCSR456BRN", "assay_title": "Histone ChIP-seq", "biosample_summary": "brain", "target": "H3K27ac"}
  ],
  "total": 24,
  "limit": 25,
  "offset": 0,
  "has_more": false,
  "next_offset": null
}

2. Explore available ENCODE data for GTEx tissues

encode_get_facets(assay_title="Histone ChIP-seq", organism="Homo sapiens")

Expected output (facet field names are the top-level keys):

{
  "biosample_ontology.organ_slims": [
    {"term": "brain", "count": 45},
    {"term": "liver", "count": 18}
  ]
}

3. Get file details for expression comparison

encode_list_files(experiment_accession="ENCSR456BRN", file_format="bed", output_type="IDR thresholded peaks", assembly="GRCh38")

Expected output (a JSON array of file records; fields abridged):

[
  {"accession": "ENCFF789IDR", "output_type": "IDR thresholded peaks", "file_format": "bed", "file_type": "bed narrowPeak", "file_size_human": "1.2 MB"}
]

Integration

Presenting Results

Expression Summary Table

Key Numbers to Report

• Gene symbol and Ensembl ID
• TPM in target tissue and rank across all 54 tissues
• Tissue specificity (tau index)
• Number of tissues where gene is expressed (TPM > 1)
• Whether ENCODE regulatory data is concordant
• eQTL count in target tissue (if queried)

Pitfalls & Edge Cases

• GTEx tissue names ≠ ENCODE organ names: GTEx uses specific tissue subregions (e.g., "Brain - Cortex", "Brain - Cerebellum") while ENCODE uses broader organ terms ("brain"). Map carefully using the GTEx tissue-to-ENCODE organ mapping table.
• GTEx v8 vs v10 coordinate differences: GTEx v8 uses GRCh38 but gene models differ from v10. Ensure you match the GTEx version to your GENCODE annotation version.
• Bulk tissue heterogeneity: GTEx expression represents a mixture of cell types. A gene with "low" GTEx expression may be highly expressed in a rare cell type. Use single-cell deconvolution for cell-type resolution.
• eQTL effect sizes are tissue-specific: A variant that is an eQTL in liver may not be one in pancreas. Never assume eQTL effects generalize across tissues — always check the specific tissue of interest.
• TPM normalization caveats: GTEx TPM values cannot be directly compared across tissues with very different transcriptome compositions. Use the GTEx-provided median TPM or normalized values for cross-tissue comparisons.

Related Skills

• variant-annotation — Full variant interpretation workflow using ENCODE functional data
• disease-research — Disease-focused ENCODE analysis workflows
• regulatory-elements — Characterizing ENCODE regulatory elements near expressed genes
• cellxgene-context — Single-cell expression context when GTEx bulk is insufficient
• cross-reference — Linking ENCODE experiments to GTEx and other external databases
• ensembl-annotation — Gene annotation, VEP, and coordinate conversion
• publication-trust — Verify literature claims backing analytical decisions

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