基因盛宴：功能富集分析解读中以基因为核心的关键步骤。

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

作者信息

Taylor Avigail, Macaulay Valentine M, Miossec Matthieu J, Maurya Anand K, Buffa Francesca M

机构信息

Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom.

Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom.

出版信息

Bioinformatics. 2025 Mar 4;41(3). doi: 10.1093/bioinformatics/btaf100.

DOI:10.1093/bioinformatics/btaf100

PMID:40037522

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11919446/

Abstract

SUMMARY

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.

AVAILABILITY AND IMPLEMENTATION

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.

摘要

GeneFEAST是用Python实现的以基因为中心的功能富集分析汇总和可视化工具，可应用于上游功能富集分析（FEA）管道产生的大型功能富集分析（FEA）结果。它生成一份系统的、可导航的HTML报告，便于识别可能驱动多种富集的基因集，并探索最初用于识别输入基因的基因水平定量数据。此外，GeneFEAST可以并列多个研究的FEA结果，从而能够突出在多种条件中至少一种条件下差异表达且在这些条件下产生共同富集的基因之间的基因表达模式。因此，GeneFEAST提供了一种新颖、有效的方法来处理将许多重叠的FEA结果与其基础基因和数据相联系的复杂性，推进以基因为中心的假设，并为下游验证实验提供关键信息。