SKILL.md

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:

POST gene list to /addList -- returns a userListId token
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]
  ]
}

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

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

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

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

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

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

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'])}")

43-wentorai-research-plugins-skills-domains-biomedical-enrichr-api

Summary

SKILL.md

Enrichr Gene Set Enrichment Analysis API

Overview

Two-Step Workflow

Core Endpoints

Base URL

Step 1: Submit Gene List

Step 2: Retrieve Enrichment Results

View Submitted Gene List

Export Results as TSV

List Available Libraries

Available Libraries (225 Total)

Pathway Databases

Gene Ontology

Disease and Phenotype

Transcription Factor and Epigenomics

Cell Type and Tissue

Cancer and Drug

Rate Limits

Academic Use Cases

Python Usage

References

Related skills