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GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation
Avigail Taylor1,2,3, Valentine M Macaulay3, Matthieu J Miossec2
1Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom.
Bioinformatics (Oxford, England)
|March 4, 2025
Summary
GeneFEAST is a Python tool that summarizes and visualizes gene-centric functional enrichment analysis (FEA) results. It helps identify key genes across multiple studies and conditions, aiding hypothesis generation and validation.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Functional enrichment analysis (FEA) generates large, complex datasets.
- Integrating FEA results from multiple studies is challenging.
- Identifying gene sets driving multiple enrichments requires effective tools.
Purpose of the Study:
- To introduce GeneFEAST, a novel gene-centric tool for summarizing and visualizing FEA results.
- To facilitate the identification of gene sets responsible for multiple biological enrichments.
- To enable comparative analysis of FEA results across different studies and conditions.
Main Methods:
- GeneFEAST is implemented in Python.
- It generates systematic, navigable HTML reports.
- The tool can juxtapose FEA results from multiple studies.
Main Results:
- GeneFEAST simplifies the identification of gene sets driving multiple enrichments.
- It allows exploration of gene-level quantitative data.
- The tool highlights patterns of gene expression across conditions and shared enrichments.
Conclusions:
- GeneFEAST provides an effective method for managing complex FEA data.
- It advances gene-centric hypothesis generation.
- The tool offers crucial information for downstream experimental validation.
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