WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit
By Jing Wang, S. Vasaikar, Zhiao Shi, M. Greer, Bing Zhang. Nucleic Acids Res., 2017
1,084 citations recorded by the source corpus.
Abstract Functional enrichment analysis has played a key role in the biological interpretation of high-throughput omics data. As a long-standing and widely used web application for functional enrichment analysis, WebGestalt has been constantly updated to satisfy the needs of biologists from different research areas. WebGestalt 2017 supports 12 organisms, 324 gene identifiers from various databases and technology platforms, and 150 937 functional categories from public databases and computational analyses. Omics data with gene identifiers not supported by WebGestalt and functional categories not included in the WebGestalt database can also be uploaded for enrichment analysis. In addition to the Over-Representation Analysis in the previous versions, Gene Set Enrichment Analysis and Network Topology-based Analysis have been added to WebGestalt 2017, providing complementary approaches to the interpretation of high-throughput omics data. The new user-friendly output interface and the GOView tool allow interactive and efficient exploration and comparison of enrichment results. Thus, WebGestalt 2017 enables more comprehensive, powerful, flexible and interactive functional enrichment analysis. It is freely available at http://www.webgestalt.org.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/501138dcc6d2622f6a0dd6a053132b5b579a0a02
How researchers see this paper
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In Molecular Biology
- Alexey I. Nesvizhskii
- G. Way
- Casey S. Greene
- Dmitry Avtonomov
- Guangyan Zhou
- Yuxing Liao
- Robert E. W. Hancock
- Zhiao Shi
Citations in the corpus
- NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis (2019, methodology): “Table 1 shows the comparisons between NetworkAnalyst 3.0 and several other well-known web-based tools dedicated to functional profiling of transcriptomics data, including WebGestalt (29), g:Profiler (30) and Enrichr (31).”
- Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “The significantly differentially expressed gene lists (fold change > = 2 and FDR 0.05) were used to perform overrepresentation enrichment analysis (ORA) implemented in WebGestaltR (Wang et al., 2017), in which the parameters were set to use 9190 background genes and the combined KEGG/HALLMARK/Reac”
- WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics (2024, methodology): “For users who are new to these methods, we recommend referring to previous publications from our team and other researchers ( 3 ,5 ).”
- Accurate and efficient detection of gene fusions from RNA sequencing data (2021, methodology): “When no KRAS missense mutation was found in the RNA-seq data, the mutation status of KRAS was taken from the respective study, whenever available (Barretina et al. 2012; Witkiewicz et al. 2016; Horak et al. 2017; Nicolle et al. 2017; The Cancer Genome Atlas Research Network 2017; Aung et al. 2018).”
- Evaluating deep variational autoencoders trained on pan-cancer gene expression (2017, methodology): “To characterize the patterns, we performed pathway overrepresentation analyses (ORA) [26] over Gene Ontology (GO) terms [27] using the high weight genes ( > 2 .”