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Enrichr-KG: bridging enrichment analysis across multiple libraries
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 enri...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320098/ https://www.ncbi.nlm.nih.gov/pubmed/37166973 http://dx.doi.org/10.1093/nar/gkad393 |
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author | Evangelista, John Erol Xie, Zhuorui Marino, Giacomo B Nguyen, Nhi Clarke, Daniel J B Ma’ayan, Avi |
author_facet | Evangelista, John Erol Xie, Zhuorui Marino, Giacomo B Nguyen, Nhi Clarke, Daniel J B Ma’ayan, Avi |
author_sort | Evangelista, John Erol |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10320098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103200982023-07-06 Enrichr-KG: bridging enrichment analysis across multiple libraries Evangelista, John Erol Xie, Zhuorui Marino, Giacomo B Nguyen, Nhi Clarke, Daniel J B Ma’ayan, Avi Nucleic Acids Res Web Server Issue 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. Oxford University Press 2023-05-11 /pmc/articles/PMC10320098/ /pubmed/37166973 http://dx.doi.org/10.1093/nar/gkad393 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Web Server Issue Evangelista, John Erol Xie, Zhuorui Marino, Giacomo B Nguyen, Nhi Clarke, Daniel J B Ma’ayan, Avi Enrichr-KG: bridging enrichment analysis across multiple libraries |
title | Enrichr-KG: bridging enrichment analysis across multiple libraries |
title_full | Enrichr-KG: bridging enrichment analysis across multiple libraries |
title_fullStr | Enrichr-KG: bridging enrichment analysis across multiple libraries |
title_full_unstemmed | Enrichr-KG: bridging enrichment analysis across multiple libraries |
title_short | Enrichr-KG: bridging enrichment analysis across multiple libraries |
title_sort | enrichr-kg: bridging enrichment analysis across multiple libraries |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320098/ https://www.ncbi.nlm.nih.gov/pubmed/37166973 http://dx.doi.org/10.1093/nar/gkad393 |
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