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Updated: Apr 24, 2026
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
Published on: August 16, 2017
Ten common mistakes that could ruin your enrichment analysis
Anusuiya Bora1,2, Matthew McKenzie1,3, Mark Ziemann1,2
1School of Life and Environmental Sciences, Faculty of Science, Engineering and Built Environment, Deakin University, Melbourne, Australia.
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.
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