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

TL;DR

GeneFEAST is a Python-based tool that summarizes, visualizes, and compares large functional enrichment analysis results at the gene level, aiding interpretation and hypothesis generation.

Contribution

It introduces a novel, comprehensive approach for gene-centric summarization and comparison of enrichment results, enhancing analysis of complex genomic data.

Findings

Produces systematic, navigable HTML reports for enrichment results.

Enables comparison of multiple FEA studies to identify shared gene patterns.

Facilitates exploration of gene-level data to support downstream validation.

Abstract

Summary: GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarisation and visualisation 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 compare FEA results from multiple studies, making it possible, for example, to highlight patterns of gene expression amongst genes commonly differentially expressed in two sets of conditions, and giving rise to shared enrichments under those conditions. 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: GeneFEAST is available at https://github.com/avigailtaylor/GeneFEAST Contact: avigail.taylor@well.ox.ac.uk