ArticleBMC bioinformatics2021
Popularity and performance of bioinformatics software: the case of gene set analysis.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Gene expression signatures of response to fluoxetine treatment: systematic review and meta-analyses.Molecular psychiatry · 2025Pooled it
- Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis.PLoS computational biology · 2022Pooled it
- A gene set collection of human RBP target genes.Scientific data · 2026Article
- Hyperpathway: visualizing organization of pathway-molecule enriched interactions in omics studies via hyperbolic bipartite network embedding.NPJ systems biology and applications · 2026Article
- GEDO: topology-based inference of gene module activity in Sjögren's disease.Briefings in bioinformatics · 2026Article
- Ten common mistakes that could ruin your enrichment analysis.PLoS computational biology · 2026Article
- A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026Article
- Using user-centered design to better understand challenges faced during genetic analyses by novice genomic researchers.Frontiers in bioinformatics · 2026Article
- GeDi: simplifying gene set distances for enhanced omics interpretation in R/Bioconductor.BMC bioinformatics · 2025Article
- Impact of polymorphisms on gene expression and splicing in response to exercise and diet-induced weight loss in human skeletal muscle tissues.Cell genomics · 2025Article
- SomaModules: A Pathway Enrichment Approach Tailored to SomaScan Data.Journal of proteome research · 2025Article
- Gene module reconstruction identifies cellular differentiation processes and the regulatory logic of specialized secretion in zebrafish.Developmental cell · 2025Article
- To Tweak or Not to Tweak. How Exploiting Flexibilities in Gene Set Analysis Leads to Overoptimism.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Direction-aware functional class scoring enrichment analysis of infinium DNA methylation data.Epigenetics · 2024Article
- Critical Differential Expression Assessment for Individual Bulk RNA-Seq Projects.bioRxiv : the preprint server for biology · 2024Article
- Two subtle problems with overrepresentation analysis.Bioinformatics advances · 2024Article
- Article
- Gene module reconstruction elucidates cellular differentiation processes and the regulatory logic of specialized secretion.bioRxiv : the preprint server for biology · 2023Article
- Article
- Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq.Advanced genetics (Hoboken, N.J.) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundGene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies.
resultsRegarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods ("GSA-BenchmarKING"). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods.
conclusionsThe above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.