enrichr-api

Enrichr Gene Set Enrichment Analysis API

Overview

Enrichr is the most widely used gene set enrichment analysis tool, developed by the Ma'ayan Lab at the Icahn School of Medicine at Mount Sinai. It tests whether a user-supplied gene list is statistically over-represented in curated gene set libraries spanning pathways, ontologies, transcription factor targets, disease associations, and cell types. The API provides access to 225 background libraries covering over 500,000 annotated gene sets. Free, no authentication required.

Two-Step Workflow

Enrichr uses a submit-then-query pattern:

1. POST gene list to /addList -- returns a userListId token
2. GET enrichment from /enrich using that token and a chosen library

The userListId persists on the server, so you can run multiple library queries against the same submission without re-uploading.

Core Endpoints

Base URL

https://maayanlab.cloud/Enrichr

Step 1: Submit Gene List

curl -X POST "https://maayanlab.cloud/Enrichr/addList" \
  -F "list=BRCA1
BRCA2
TP53
EGFR
MYC
PTEN
AKT1
KRAS
PIK3CA
RAF1" \
  -F "description=cancer_genes"

Response:

{
  "shortId": "8619200cc78f1513ff1029a04af90ad7",
  "userListId": 124544426
}

Genes are newline-separated. The request must use multipart/form-data (the -F flag), not application/x-www-form-urlencoded.

Step 2: Retrieve Enrichment Results

curl "https://maayanlab.cloud/Enrichr/enrich?userListId=124544426&backgroundType=KEGG_2021_Human"

Response (first 3 of 143 results):

{
  "KEGG_2021_Human": [
    [1, "Breast cancer", 3.37e-22, 198530.0, 9815800.25,
     ["PIK3CA","MYC","PTEN","AKT1","KRAS","BRCA1","BRCA2","RAF1","TP53","EGFR"],
     4.82e-20, 0, 0],
    [2, "Endometrial cancer", 1.35e-19, 1595.2, 69306.12,
     ["PIK3CA","MYC","PTEN","AKT1","KRAS","RAF1","TP53","EGFR"],
     9.68e-18, 0, 0],
    [3, "Central carbon metabolism in cancer", 6.66e-19, 1285.68, 53809.88,
     ["PIK3CA","MYC","PTEN","AKT1","KRAS","RAF1","TP53","EGFR"],
     3.17e-17, 0, 0]
  ]
}

Each result array contains: [rank, term_name, p_value, z_score, combined_score, overlapping_genes, adjusted_p_value, old_p_value, old_adjusted_p_value].

View Submitted Gene List

curl "https://maayanlab.cloud/Enrichr/view?userListId=124544426"

{
  "genes": ["PIK3CA","MYC","AKT1","PTEN","BRCA1","KRAS","BRCA2","EGFR","TP53","RAF1"],
  "description": "cancer_genes"
}

Export Results as TSV

curl "https://maayanlab.cloud/Enrichr/export?userListId=124544426&backgroundType=KEGG_2021_Human&filename=results" \
  -o enrichr_results.txt

List Available Libraries

curl "https://maayanlab.cloud/Enrichr/datasetStatistics"

Returns metadata for all 225 libraries, each entry containing libraryName, numTerms, geneCoverage, and genesPerTerm.

Available Libraries (225 Total)

Pathway Databases

Gene Ontology

Disease and Phenotype

Transcription Factor and Epigenomics

Cell Type and Tissue

Cancer and Drug

Rate Limits

• No authentication or API key required
• No officially published rate limits, but automated queries should include reasonable delays (1-2 seconds between requests)
• Very large gene lists (>3,000 genes) may time out on some libraries
• The userListId persists server-side; avoid re-submitting the same list repeatedly

Academic Use Cases

• Differential expression follow-up: Submit DEGs from RNA-seq to identify enriched pathways and GO terms
• GWAS hit annotation: Map GWAS-significant genes to disease phenotypes via DisGeNET or GWAS_Catalog
• Drug target discovery: Cross-reference gene signatures against DGIdb_Drug_Targets for druggable candidates
• Transcription factor analysis: Identify upstream regulators via ChEA or ENCODE TF libraries

Python Usage

import requests

ENRICHR_URL = "https://maayanlab.cloud/Enrichr"


def submit_gene_list(genes: list[str], description: str = "") -> int:
    """Submit a gene list to Enrichr, return userListId."""
    payload = {
        "list": (None, "\n".join(genes)),
        "description": (None, description),
    }
    resp = requests.post(f"{ENRICHR_URL}/addList", files=payload)
    resp.raise_for_status()
    return resp.json()["userListId"]


def get_enrichment(user_list_id: int, library: str) -> list[dict]:
    """Retrieve enrichment results for a given library."""
    resp = requests.get(
        f"{ENRICHR_URL}/enrich",
        params={"userListId": user_list_id, "backgroundType": library},
    )
    resp.raise_for_status()
    data = resp.json()

    results = []
    for entry in data.get(library, []):
        results.append({
            "rank": entry[0],
            "term": entry[1],
            "p_value": entry[2],
            "z_score": entry[3],
            "combined_score": entry[4],
            "genes": entry[5],
            "adj_p_value": entry[6],
        })
    return results


def get_libraries() -> list[dict]:
    """List all available Enrichr libraries."""
    resp = requests.get(f"{ENRICHR_URL}/datasetStatistics")
    resp.raise_for_status()
    return resp.json()["statistics"]


# Example: enrichment analysis of cancer-related genes
genes = ["BRCA1", "BRCA2", "TP53", "EGFR", "MYC",
         "PTEN", "AKT1", "KRAS", "PIK3CA", "RAF1"]

list_id = submit_gene_list(genes, "cancer_genes")
print(f"Submitted gene list, ID: {list_id}")

# Query KEGG pathways
kegg = get_enrichment(list_id, "KEGG_2021_Human")
print(f"\nTop 5 KEGG pathways ({len(kegg)} total):")
for r in kegg[:5]:
    print(f"  {r['rank']}. {r['term']}")
    print(f"     p={r['p_value']:.2e}, adj_p={r['adj_p_value']:.2e}, "
          f"genes={','.join(r['genes'][:5])}...")

# Query GO Biological Process
go_bp = get_enrichment(list_id, "GO_Biological_Process_2023")
print(f"\nTop 5 GO Biological Processes ({len(go_bp)} total):")
for r in go_bp[:5]:
    print(f"  {r['rank']}. {r['term']}")
    print(f"     p={r['p_value']:.2e}, genes={','.join(r['genes'])}")

References

• Enrichr Web App
• Enrichr API Docs
• Chen, E.Y. et al. (2013). "Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool." BMC Bioinformatics 14:128.
• Kuleshov, M.V. et al. (2016). "Enrichr: a comprehensive gene set enrichment analysis web server 2016 update." Nucleic Acids Res. 44(W1).
• Xie, Z. et al. (2021). "Gene Set Knowledge Discovery with Enrichr." Current Protocols 1(3):e90.