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. 2019 Jul 2;47(W1):W199-W205.
doi: 10.1093/nar/gkz401.

WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs

Yuxing Liao  1 , Jing Wang  1 , Eric J Jaehnig  1 , Zhiao Shi  1 , Bing Zhang  1   2
Affiliations

WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs

Yuxing Liao et al. Nucleic Acids Res. .

Abstract

WebGestalt is a popular tool for the interpretation of gene lists derived from large scale -omics studies. In the 2019 update, WebGestalt supports 12 organisms, 342 gene identifiers and 155 175 functional categories, as well as user-uploaded functional databases. To address the growing and unique need for phosphoproteomics data interpretation, we have implemented phosphosite set analysis to identify important kinases from phosphoproteomics data. We have completely redesigned result visualizations and user interfaces to improve user-friendliness and to provide multiple types of interactive and publication-ready figures. To facilitate comprehension of the enrichment results, we have implemented two methods to reduce redundancy between enriched gene sets. We introduced a web API for other applications to get data programmatically from the WebGestalt server or pass data to WebGestalt for analysis. We also wrapped the core computation into an R package called WebGestaltR for users to perform analysis locally or in third party workflows. WebGestalt can be freely accessed at http://www.webgestalt.org.

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Figures

Figure 1.
Figure 1.
New and improved visualizations in the result page of WebGestalt 2019. (A) Table summary of significant results. The options of choosing reduced subsets are also shown. (B) Bar chart shows enrichment ratio or NES of results with direction. (C) Customizable volcano plot. Inset shows initial layout for comparison. (D) New implementation of DAG. Now it is more compact highlighting enriched nodes. (E) Venn diagram shows the overlap between the gene set in the input and in the reference. (F) GSEA enrichment plot. (G) Pathway view of WikiPathways highlighting leading edge genes based on score.
Figure 2.
Figure 2.
The number of all significant results are compared with the numbers with redundancy reduction regarding different thresholds and datasets including (A) Gene Ontology (GO) biological process, (B) GO molecular function, (C) KEGG pathway, and (D) WikiPathways.
Figure 3.
Figure 3.
The redesigned system centered around the WebGestaltR package allows easy access of users and programs.
See this image and copyright information in PMC

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