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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
Published on: August 16, 2017
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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.
Plos Computational Biology
|April 22, 2026
Summary
Functional enrichment analysis (FEA) is a common bioinformatics technique. This study identifies ten common mistakes in FEA to improve the accuracy and reliability of genomics research findings.
Area of Science:
- Bioinformatics
- Genomics
- Systems Biology
Background:
- Functional enrichment analysis (FEA) is widely used to interpret complex genomics data.
- FEA summarizes data into biological pathways like metabolism, signaling, and immune responses.
- Despite its routine use, common errors can compromise FEA effectiveness.
Purpose of the Study:
- To identify common pitfalls in functional enrichment analysis.
- To provide practical guidance for mitigating these errors.
- To enhance the reliability of bioinformatics research.
Main Methods:
- Review of common errors in functional enrichment analysis.
- Identification of ten prevalent mistakes in research articles.
- Development of practical recommendations for users and tool designers.
Main Results:
- Ten common mistakes that undermine FEA effectiveness were identified.
- These errors stem from poor tool design and user unawareness.
- Specific examples of flawed FEA applications are discussed.
Conclusions:
- Awareness of common FEA pitfalls is crucial for accurate genomics data interpretation.
- Implementing practical mitigation strategies can significantly improve FEA results.
- This work aims to enhance the quality and reproducibility of bioinformatics studies.
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