Appropriate threshold setting and multiple methods combination may improve reproducibility of gene ontology enrichment analysis

Transcriptome analyses are widely used for biological research. Gene ontology (GO) enrichment analysis is often used for effectively analyzing large data matrices. However, various software and methods can be used for performing GO enrichment analyses, which might lead to different conclusions. To date, there is no agreement whatsoever in the scientific research community about standards and processes for analysis; moreover, the description about such analyses in previous research is often brief, causing difficulties in both research reproducibility and manuscript review. Herein, we introduce the advantages of different tools and methods, while the limitations or problems are stated. We found that the essential key to improving the reproducibility and accuracy of GO enrichment analyses was to set appropriate thresholds in data processing and might combine different GO methods to avoid the limitations of each one. Finally, research associations might need to consider draft-standardized generic transcriptomic analysis standards to promote advances in biological research.

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Publication Details

Journal
Biochemistry and Biophysics Reports
Published
2026-04-21
DOI
https://doi.org/10.1016/j.bbrep.2026.102599
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Appropriate threshold setting and multiple methods combination may improve reproducibility of gene ontology enrichment analysis

Qi Guo, Tian Fu, Qingyin Yu, Yunlu Zhao et al.
Biochemistry and Biophysics Reports
Bioinformatics and Genomic Networks
article

Appropriate threshold setting and multiple methods combination may improve reproducibility of gene ontology enrichment analysis

Qi Guo, Tian Fu, Qingyin Yu, Yunlu Zhao, Na Wang
article en

Abstract

Transcriptome analyses are widely used for biological research. Gene ontology (GO) enrichment analysis is often used for effectively analyzing large data matrices. However, various software and methods can be used for performing GO enrichment analyses, which might lead to different conclusions. To date, there is no agreement whatsoever in the scientific research community about standards and processes for analysis; moreover, the description about such analyses in previous research is often brief, causing difficulties in both research reproducibility and manuscript review. Herein, we introduce the advantages of different tools and methods, while the limitations or problems are stated. We found that the essential key to improving the reproducibility and accuracy of GO enrichment analyses was to set appropriate thresholds in data processing and might combine different GO methods to avoid the limitations of each one. Finally, research associations might need to consider draft-standardized generic transcriptomic analysis standards to promote advances in biological research.

Biochemistry and Biophysics ReportsVol. 46
Hebei University of Chinese Medicine (CN)
Department of Education of Hebei Province
Openalex Percentile: Top 18%
Bioinformatics and Genomic Networks
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