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

Library	Terms	Genes
KEGG_2026	352	8,110
KEGG_2021_Human	320	8,078
WikiPathways_2024_Human	829	8,281
Reactome_Pathways_2024	2,105	11,671
BioCarta_2016	237	1,348

Gene Ontology

Library	Terms	Genes
GO_Biological_Process_2025	5,343	14,674
GO_Molecular_Function_2025	1,174	11,484
GO_Cellular_Component_2025	468	11,501

Disease and Phenotype

Library	Terms	Genes
DisGeNET	9,828	17,464
GWAS_Catalog_2025	2,369	15,030
ClinVar_2025	609	3,481
OMIM_Disease	90	1,759
Human_Phenotype_Ontology	1,779	3,096

Transcription Factor and Epigenomics

Library	Terms	Genes
ChEA_2022	757	18,365
ENCODE_TF_ChIP-seq_2015	816	26,382
JASPAR_PWM_Human_2025	675	18,518

Cell Type and Tissue

Library	Terms	Genes
CellMarker_2024	1,692	12,642
ARCHS4_Tissues	108	21,809
Human_Gene_Atlas	84	13,373

Cancer and Drug

Library	Terms	Genes
MSigDB_Hallmark_2020	50	4,383
MSigDB_Oncogenic_Signatures	189	11,250
DGIdb_Drug_Targets_2024	659	2,513

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.