WEB-based GEne SeT AnaLysis Toolkit
|Translating gene lists into biological insights...|
WebGestalt 2019 has been officially released with updated data. The 2017 version can be accessed through the link.
WebGestalt (WEB-based Gene SeT AnaLysis Toolkit) is a functional enrichment analysis web tool, which has on average 26,000 unique users from 144 countries and territories per year according to Google Analytics. The WebGestalt 2005, WebGestalt 2013 and WebGestalt 2017 papers have been cited in more than 2,500 scientific papers according to Google Scholar.
WebGestalt 2019 significantly improved the output report with emphasis on providing user-friendly interfaces which could directly translate into publication-ready figures. The R package WebGestaltR has been updated to work with the new verison, which provides an interface to integrate into other pipelines or run batch jobs locally. We also support loading data from third-party websites or services through an API to perform enrichment analysis. WebGestalt 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).
Data sources for WebGestalt 2019 was updated on 01/14/2019, which supports 12 organisms, 354 gene identifiers from various databases and technology platforms, and 321,251 functional categories from public databases and computational analyses. Experimental data from organisms or with gene identifiers not covered by the WebGestalt database can also be analyzed in WebGestalt. We recently added phophosite data for kinase target enrichment analysis. Information in this version was collected from the following resources:
Gene Ontology (Daily build accessed on 01/14/2019.)
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 (Build 3.5.167, December 2018) using NetSAM.
microRNA target: MSigDB (MSigDB database v6.2, July 2018)
Transcript factor target: MSigDB (MSigDB database v6.2, July 2017)
Mammalian protein complexes: CORUM (Release 3.0, 09/03/2018)
Chromosomal location (NCBI, Accessed on 12/20/2018)
Kinase-specific phosphorylation sites are from RegPhos 2.0
PTM signatures are from PTMsigDB v1.8.1
Browser support: We strongly recommend using evergreen browsers, such as Chrome and Firefox, although most functionalities should work on IE > 10. Version: 14a115f1