Year: 2026
Type: article
Abstract: Functional enrichment analysis (FEA) is an incredibly powerful way to summarise complex genomics data into information about the regulation of biological pathways including cellular metabolism, signalling and immune responses. About 10,000 scientific articles describe using FEA each year, making it among the most used techniques in bioinformatics. While FEA has become a routine part of workflows via myriad software packages and easy-to-use websites, mistakes can easily creep in due to poor tool design and unawareness among users of pitfalls. Here we outline ten mistakes that undermine the effectiveness of FEA which we commonly see in research articles. We provide practical advice on their mitigation.
Source: PLoS Computational Biology
Authors: Anusuiya Bora, Matthew McKenzie, Mark Ziemann
Institutions: Burnet Institute, Deakin University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
OpenAlex ID: W7155168775