WEB-based GEne SeT AnaLysis Toolkit
Translating gene lists into biological insights...

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To visualize and compare multiple GO term lists, please use GOView.
To discuss the use and development of WebGestalt or GOView, please join the new User Forum.


WebGestalt is a "WEB-based GEne SeT AnaLysis Toolkit". It is designed for functional genomic, proteomic and large-scale genetic studies from which large number of gene lists (e.g. differentially expressed gene sets, co-expressed gene sets etc) are continuously generated. WebGestalt incorporates information from different public resources and provides an easy way for biologists to make sense out of gene lists.

There are many web-based enrichment analysis tools. Please follow this link to find features that are unique to WebGestalt as compared to several popular tools.

Please follow the workflow below to analyze your gene lists:

1. Select the organism of interest.
2. Upload a gene/protein list in the txt format, one ID per row. Optionally, a value can be provided for each ID. In this case, put the ID and value in the same row and separate them by a tab. Then pick the ID type that corresponds to the list of IDs.
3. Categorize the uploaded ID list based upon GO Slim.
4. Analyze the uploaded ID list for for enrichment in various biological contexts. You will need to select an appropriate predefined reference set or upload a reference set. If a customized reference set is uploaded, ID type also needs to be selected. After this, select the analysis parameters (e.g., significance level, multiple test adjustment method, etc.).
5. Retrieve enrichment results by opening the respective results files. You may also open and/or download a TSV file, or download the zipped results to a directory on your desktop.


The current version of WebGestalt was updated on 1/30/2013. It supports eight organisms including human, mouse, rat, worm, fly, yeast, dog, and zebrafish. Information in this version was collected from the following resources:

ID mapping: NCBI Gene (10/26/2012), NCBI GEO (12/10/2012), Ensembl (version 68, 11/23/2012), MGI (10/21/2012), SGD (10/21/2012), Affymetrix (1/11/2013), Illumina (1/11/2013), IPI (11/21/2011), NCBI dbSNP (12/06/2012).

Gene Ontology: Gene Ontology (version 1.2, 11/11/2012).

Pathways: KEGG (03/21/2011), Pathway Commons (11/11/2012), WikiPathways (11/11/2012).

Regulatory modules (Motif gene sets): MSigDB (11/11/2012).

Protein-protein interaction modules: Protein-protein interaction data were downloaded from HPRD (11/11/2012), BioGrid(11/11/2012), BOND(11/11/2012), DIP(11/11/2012), IntAct(11/11/2012), MINT(11/11/2012), and Reactome(11/11/2012). Only interactions with publication support were retained. Protein-protein interaction modules at different hierarchical levels were identified by iterative partitioning of the integrated protein-protein interaction network using the NetSAM package (detailed description of the NetSAM package can be found in our recently published Nature Methods paper "NetGestalt: integrating multidimensional omics data over biological networks").

Disease and drug associated genes: disease and drug terms were downloaded from PharmGKB (1/26/2013), genes associated with individual disease and drug terms were inferred using GLAD4U (1/26/2013).

Cytogenetic band: NCBI Gene (10/26/2012).

Phenotype: Mammalian Phenotype Ontology (04/10/2013) and Human Phenotype Ontology (04/10/2013).

PheWAS: PheWAS Catalog (05/20/2014).


For gene identifiers that are not supported by WebGestalt, users may consider using more advanced ID conversion tools such as BioMart to convert their IDs to Entrez Gene IDs for uploading to WebGestalt.


Zhang, B., Kirov, S.A., Snoddy, J.R. (2005). WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res, 33(Web Server issue), W741-748.

Wang, J., Duncan, D., Shi, Z., Zhang, B. (2013). WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res, 41 (Web Server issue), W77-83.

Usage statistics since April 14, 2010

The local usage count is 18154 and the last date of local usage: 07/27/23
The remote usage count is 11707 and the last date of remote usage: 12/07/23


Update 2015
GOView, a web application tool for visualizing and comparing multiple GO term lists, are now available in WebGestalt.

Update 2014
PheWAS data are now available in WebGestalt.

Update 2013

Users can now export enriched network modules to different file formats, including png, svg, pdf, xgmml, graphml, and sif.

WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013 has been published in Nucleic Acids Research.

1) Removed the CAPTCHA;
2) Merged the pages for selecting organism and uploading gene/protein list;
3) Merged the pages for functional category selection and parameter setting;
4) Added an option for pasting gene identifiers directly;
5) Added phenotype data downloaded from Mammalian Phenotype Ontology and Human Phenotype Ontology;
6) Added link-outs for Pathway Commons enrichment results;
7) Added a "Related Function" feature to show GO terms that are most significantly correlated with the PPI modules.

The new version of WebGestalt has further increased the coverage of gene identifiers and function categories in various biological context. Currently, WebGestalt supports 201 types of gene identifiers, including gene and protein IDs from major public databases and microarray probe IDs from Affematrix, Agilent, Illumina and Codelink. WebGestalt also contains 59,589 functional categories derived from centrally and publicly curatd databases as well as computational analyses, including Gene Ontology, KEGG, Pathway Commons, WikiPathways,Transcription factor target, microRNA target, Hierarchial protein interaction network modules, Cytogenetic band, Disease associated genes and Drug associated genes. In addition, hierarchical network visualization feature has been implemented to help users better understand the network module enrichment analysis results.

More News

WebGestalt is currently developed and maintained by Jing Wang and Bing Zhang at the Zhang Lab. Other people who have made significant contribution to the project include Dexter Duncan, Stefan Kirov, Zhiao Shi, and Jay Snoddy.

Funding credits: NIH/NIAAA (U01 AA016662, U01 AA013512); NIH/NIDA (P01 DA015027); NIH/NIMH (P50 MH078028, P50 MH096972); NIH/NCI (U24 CA159988); NIH/NIGMS (R01 GM088822).