Enrichr-KG: bridging enrichment analysis across multiple libraries

Abstract Gene and protein set enrichment analysis is a critical step in the analysis of data collected from omics experiments. Enrichr is a popular gene set enrichment analysis web-server search engine that contains hundreds of thousands of annotated gene sets. While Enrichr has been useful in provi...

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Vydáno v:Nucleic acids research Ročník 51; číslo W1; s. W168 - W179
Hlavní autoři: Evangelista, John Erol, Xie, Zhuorui, Marino, Giacomo B, Nguyen, Nhi, Clarke, Daniel J B, Ma’ayan, Avi
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 05.07.2023
Témata:
Databases, Factual
Gene Library
Internet
Proteins
Search Engine
Software
Web Server Issue
ISSN:0305-1048, 1362-4962, 1362-4962
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Shrnutí:Abstract Gene and protein set enrichment analysis is a critical step in the analysis of data collected from omics experiments. Enrichr is a popular gene set enrichment analysis web-server search engine that contains hundreds of thousands of annotated gene sets. While Enrichr has been useful in providing enrichment analysis with many gene set libraries from different categories, integrating enrichment results across libraries and domains of knowledge can further hypothesis generation. To this end, Enrichr-KG is a knowledge graph database and a web-server application that combines selected gene set libraries from Enrichr for integrative enrichment analysis and visualization. The enrichment results are presented as subgraphs made of nodes and links that connect genes to their enriched terms. In addition, users of Enrichr-KG can add gene-gene links, as well as predicted genes to the subgraphs. This graphical representation of cross-library results with enriched and predicted genes can illuminate hidden associations between genes and annotated enriched terms from across datasets and resources. Enrichr-KG currently serves 26 gene set libraries from different categories that include transcription, pathways, ontologies, diseases/drugs, and cell types. To demonstrate the utility of Enrichr-KG we provide several case studies. Enrichr-KG is freely available at: https://maayanlab.cloud/enrichr-kg. Graphical Abstract Graphical Abstract
Bibliografie:ObjectType-Article-1
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ISSN:0305-1048
1362-4962
1362-4962
DOI:10.1093/nar/gkad393