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.
Please see the User Guide for installation and usage instructions.
After installing GeneFEAST or downloading the docker container (see User Guide), you can test it using the example data below.
The following links contain example input data, instructions on how to run GeneFEAST on them, and example output GeneFEAST reports.
If you use GeneFEAST in your research, please cite the paper:
- Taylor, A., Macaulay, V.M., Maurya, A.K., Miossec, M.J., Buffa, F.M. GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation. arXiv:2309.00061
If you use an upset plot generated by GeneFEAST, please also cite:
- Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., Pfister, R. UpSet: Visualization of Intersecting Sets. IEEE Transactions on Visualization and Computer Graphics (InfoVis ‘14), vol. 20, no. 12, pp. 1983–1992, 2014. doi.org/10.1109/TVCG.2014.2346248
If you use a GO hierarchy image generated by GeneFEAST, please also cite:
- Klopfenstein, D.V., Zhang, L., Pedersen, B.S. et al. GOATOOLS: A Python library for Gene Ontology analyses. Sci Rep 8, 10872 (2018). https://doi.org/10.1038/s41598-018-28948-z
