GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation

GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarization and visualization tool that can be applied to large functional enrichment analysis (FEA) results arising from upstream FEA pipelines. It produces a systematic, navigable HTML report, making it easy to id...

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Vydané v:Bioinformatics (Oxford, England) Ročník 41; číslo 3
Hlavní autori: Taylor, Avigail, Macaulay, Valentine M, Miossec, Matthieu J, Maurya, Anand K, Buffa, Francesca M
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Oxford University Press 04.03.2025
Predmet:
Applications Note
Computational Biology - methods
Gene Expression Profiling - methods
Software
ISSN:1367-4811, 1367-4803, 1367-4811
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Popis
Shrnutí:GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarization and visualization tool that can be applied to large functional enrichment analysis (FEA) results arising from upstream FEA pipelines. It produces a systematic, navigable HTML report, making it easy to identify sets of genes putatively driving multiple enrichments and to explore gene-level quantitative data first used to identify input genes. Further, GeneFEAST can juxtapose FEA results from multiple studies, making it possible to highlight patterns of gene expression amongst genes that are differentially expressed in at least one of multiple conditions, and which give rise to shared enrichments under those conditions. Thus, GeneFEAST offers a novel, effective way to address the complexities of linking up many overlapping FEA results to their underlying genes and data, advancing gene-centric hypotheses, and providing pivotal information for downstream validation experiments. GeneFEAST GitHub repository: https://github.com/avigailtaylor/GeneFEAST; Zenodo record: 10.5281/zenodo.14753734; Python Package Index: https://pypi.org/project/genefeast; Docker container: ghcr.io/avigailtaylor/genefeast.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btaf100