Enrichr: What It Is, Inputs, Libraries, Citation

Enrichr is a free, web-based gene list enrichment analysis tool from the Ma’ayan Lab at the Icahn School of Medicine at Mount Sinai. You give it a list of gene symbols, for example the genes that changed in an RNA-seq experiment, and it tests that list against many libraries of annotated gene sets, such as pathways, ontologies and transcription-factor targets. This page explains what the tool is, what it accepts, how its related resources (Enrichr-KG, the enrichR R package and the API) differ, and how to cite it.

What Is Enrichr?

The original 2013 paper describes the tool as an “interactive and collaborative HTML5 gene list enrichment analysis tool.” Enrichment analysis asks whether a submitted gene list overlaps a predefined gene set (for example the genes annotated to one pathway) more than expected by chance. Enrichr runs that comparison across many gene-set libraries at once and returns ranked results per library. It is an interpretation step that typically follows an upstream analysis such as RNA-seq differential expression; it does not process raw sequencing data.

Who Runs Enrichr and What It Covers

Enrichr is developed in the Ma’ayan Lab (led by Avi Ma’ayan), Icahn School of Medicine at Mount Sinai, and is hosted at maayanlab.cloud/Enrichr.

Library content grows over time, so any library count is dated. The 2016 update paper reported 180,184 annotated gene sets from 102 gene set libraries. Use the live library list in the tool for the current set. Libraries include pathway and ontology resources of the kind described in our entries on Reactome and KEGG.

How to Use Enrichr

The Help Center, linked from the Enrichr site, holds the full documentation.

Open maayanlab.cloud/Enrichr. Upload a file or paste a set of gene symbols into the text box, one gene symbol per row.
Optionally supply a background gene set. A background option is available in both the web interface and the API.
Submit the list. Enrichr returns enrichment results grouped by gene-set library.
If you start from a single gene, term or variant rather than a list, Enrichr can expand it into a gene set first.
If you have a gene set of your own, the site offers a way to contribute it.

For scripted use, the Help Center’s API documentation names three endpoints: addList (submit a list), enrich (retrieve enrichment results) and export (download results). The Help Center sections are The Basics, Background Information, API Documentation, FAQ and Terms of Use.

Enrichr-KG, the R Package and the API

Enrichr-KG

Enrichr-KG is a companion resource described in Nucleic Acids Research 2023;51(W1):W168-W179 (PubMed 37166973). It lets you compare enrichment results across several Enrichr libraries and view them as knowledge-graph subgraphs, rather than reading one library at a time.

Enrichr in R

The enrichR package for R, published on CRAN, gives R access to Enrichr (see the CRAN package page), so enrichment can be run inside an analysis script. Scripted analysis supports the reproducibility habits covered in What Is Bioinformatics?.

How to Cite Enrichr

The three references Enrichr asks users to cite:

Chen EY et al. 2013. BMC Bioinformatics. (original Enrichr paper, doi:10.1186/1471-2105-14-128)
Kuleshov MV et al. 2016. Nucleic Acids Research (gkw377). (2016 update, doi:10.1093/nar/gkw377)
Xie Z et al. 2021. Current Protocols e90. (“Gene Set Knowledge Discovery with Enrichr”, doi:10.1002/cpz1.90)

General conventions for citing software and databases are summarised in FORCE11 Software Citation Principles.

Strengths and Limits

Strength: one submission is tested against many libraries, and the tool needs only gene symbols pasted into a web form.
Limit, input: Enrichr takes gene symbols, one per row, so identifiers from other systems need converting first.
Method caution, background: results depend on the background gene set. A background option exists, so state which one you used.
Method caution, versioning: because the libraries change over time (see the dated 2016 figure above), record the library names and the date you ran the analysis so the result can be reproduced.
Interpretation: enrichment output shows overlap with annotated sets, not causation. For reading the upstream differential-expression results, see volcano plots in RNA-seq.

Alternatives

Other enrichment tools exist. Each is a separate project with its own site.

Tool	What its own site says	Where
g:Profiler	Web server for functional enrichment analysis and gene list conversion	biit.cs.ut.ee/gprofiler
GSEA and MSigDB	GSEA is a computational method that tests whether a predefined gene set differs consistently between two biological states; MSigDB is a collection of annotated gene sets for use with GSEA. The GSEA software is downloaded.	gsea-msigdb.org
DAVID	Functional annotation bioinformatics resource, hosted at NIH	davidbioinformatics.nih.gov
clusterProfiler	A Bioconductor package for R	bioconductor.org

FAQ

What does Enrichr do?

It tests a pasted list of gene symbols against many annotated gene-set libraries and returns enrichment results for each library.

Who maintains Enrichr?

The Ma’ayan Lab at the Icahn School of Medicine at Mount Sinai.

What is the difference between Enrichr and Enrichr-KG?

Enrichr reports results library by library. Enrichr-KG, described in a 2023 Nucleic Acids Research paper, compares results across libraries and shows them as knowledge-graph subgraphs.

Can I use Enrichr in R or by API?

Yes. The enrichR package on CRAN provides R access, and the Help Center documents the addList, enrich and export API endpoints.

Which papers should I cite?

Chen et al. 2013 (BMC Bioinformatics), Kuleshov et al. 2016 (Nucleic Acids Research) and Xie et al. 2021 (Current Protocols), as Enrichr asks.

Sources

Checked 2026-11-07.

Enrichr: https://maayanlab.cloud/Enrichr/
Enrichr Help Center: https://maayanlab.cloud/Enrichr/help
Chen EY et al. 2013, BMC Bioinformatics: https://doi.org/10.1186/1471-2105-14-128
Kuleshov MV et al. 2016, Nucleic Acids Research: https://doi.org/10.1093/nar/gkw377
Xie Z et al. 2021, Current Protocols: https://doi.org/10.1002/cpz1.90
Enrichr-KG, Nucleic Acids Research 2023;51(W1):W168-W179: https://pubmed.ncbi.nlm.nih.gov/37166973/
enrichR on CRAN: https://cran.r-project.org/package=enrichR
g:Profiler: https://biit.cs.ut.ee/gprofiler/
GSEA and MSigDB: https://www.gsea-msigdb.org/
DAVID: https://davidbioinformatics.nih.gov/
clusterProfiler: https://bioconductor.org/packages/clusterProfiler/

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