WebGestalt (WEB-based Gene SeT AnaLysis Toolkit) is a functional enrichment analysis web tool, which has been visited 209,028 times by 84,024 unique users from 144 countries and territories since 2013 according to Google Analytics. The WebGestalt 2005 and WebGestalt 2013 papers have been cited in 1179 scientific papers since 2013 according to Google Scholar.

WebGestalt 2017 significantly increased the number of supported organisms, gene identifiers, and functional categories in WebGestalt. Notably, experimental data from organisms or with gene identifiers not covered by the WebGestalt database can also be analyzed in WebGestalt. WebGestalt also supports three well-established and complementary methods for enrichment analysis, including Over-Representation Analysis (ORA), Gene Set Enrichment Analysis (GSEA), and Network Topology-based Analysis (NTA). To facilitate easy exploration and better understanding of the enrichment results, we have revamped the output interface with a user-friendly, tab-based, and interactive report. We have also developed a companion tool GOView that can help visualize and compare multiple Gene Ontology (GO) enrichment results under the GO Directed Acyclic Graph (DAG) structure.

Data sources for WebGestalt 2017 was updated on 1/27/2017, which supports 12 organisms, 300 gene identifiers from various databases and technology platforms, and 150,937 functional categories from public databases and computational analyses. Information in this version was collected from the following resources:

• ID mapping
Affymetrix, Agilent, Illumina, ABI SOLid (Accessed on Jan, 2017), NCBI GEO (Accessed on Jan, 2017), NCBI Gene (Accessed on 7/11/2016), Biomart (biomaRt 2.32.0), dbSNP (Version 147, Accessed on 13/12/2016).
• Functional categories
• Gene Ontology (Release Oct24, 2016)
• Pathway
KEGG (Release October 1, 2016), Wikipathway (Release 05/10/2018), Reactome (Version 2016), PANTHER (Release 6/14/16)
• Network
Hierarchical mRNA co-expression modules: The modules are computationally derived from the RNA-Seq data sets across 33 TCGA (The Cancer Genome Atlas, Release 01/28/2016) cancer types. Based on the method described in our recently published paper (Proteome profiling outperforms transcriptome profiling for co-expression based gene function prediction), we first constructed the consensus co-expression network for each cancer type and then used NetSAM to identify the hierarchical co-expression modules.

Hierarchical protein interaction modules: The modules are computationally derived from the protein-protein interaction networks downloaded from BioGRID using NetSAM.

microRNA target: MSigDB (MSigDB database v6.0, 2017)

Transcript factor target: MSigDB (MSigDB database v6.0, 2017)

• Phenotype
Human Phenotype Ontology (Accessed on Jan, 2017), Mammalian Phenotype Ontology (Release MGI 6.06, October 10, 2016)
• Disease
DisgeNET (Release June 2016)
GLAD4U: Disease terms were downloaded from PharmGKB (Accessed on Jan 2017). Genes associated with individual disease term were inferred using GLAD4U.
• Drug
DrugBank (Version 5.0)
GLAD4U: Drug terms were downloaded from PharmGKB (Accessed on Jan 2017). Genes associated with individual drug term were inferred using GLAD4U.
• Chromosomal location (NCBI, Accessed on 7/11/2016)

• WebGestaltR released to CRAN. (4/27/2017)
• WebGestalt 2017 is now online. (1/31/2017)