ArticleBioinformatics advances2024
Two subtle problems with overrepresentation analysis.
Article in Bioinformatics advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- paraORA: GPU-Accelerated Exact Over-Representation Analysis for Repeated Queries of Large Gene-Set Libraries.Biology · 2026Article
- Cross-Cohort Transcriptomic Prioritization and Chemical-Provenance Assessment of Asthma Airway-Brushing Genes Overlapping HERB-AnnotatedInternational journal of molecular sciences · 2026Article
- A gene set collection of human RBP target genes.Scientific data · 2026Article
- GeneInsight: Condensing gene set knowledge via language models.PLoS computational biology · 2026Article
- Type I interferon-dependent and -independent signaling underlie autoantibody production in a murine lupus model.Journal of translational autoimmunity · 2026Article
- Circulating miR-10b-5p as a candidate biomarker of atrial fibrillation recurrence after catheter ablation: a two-phase translational study.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
- Proteomic signatures of adipocyte recruitment in breast cancer.Endocrine-related cancer · 2026Article
- A network-based map of the chemical exposome connects molecular interactions to public health.Nature communications · 2026Article
- Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.Nature communications · 2026Article
- From SNPs to pathways: a genome-wide benchmark of annotation discrepancies and their impact on protein- and pathway-level inference.BMC genomics · 2026Article
- Testing for Network Specificity in Brain-Behavior Associations Using Ordinal Dominance Curves.Human brain mapping · 2026Article
- Ten common mistakes that could ruin your enrichment analysis.PLoS computational biology · 2026Article
- Biological Functional Class Enrichment Analysis with R, an Annotated Tutorial for Bench Scientists.Methods and protocols · 2026Article
- Article
- A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026Article
- Benchmarking multiple gene ontology enrichment tools reveals high biological significance, ranking, and stringency heterogeneity among datasets.Frontiers in bioinformatics · 2026Article
- Concordant Shared Transcriptomic Signatures and Candidate Regulatory Features in Chronic Lymphocytic Leukemia and Multiple Myeloma.Oncology research · 2026Article
- Natural language processing of gene descriptions for overrepresentation analysis with GeneTEA.Genome biology · 2025Article
- Pathway Analysis Interpretation in the Multi-Omic Era.Biotech (Basel (Switzerland)) · 2025Review
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
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Overrepresentation analysis (ORA) is used widely to assess the enrichment of functional categories in a gene list compared to a background list. ORA is therefore a critical method in the interpretation of 'omics data, relating gene lists to biological functions and themes. Although ORA is hugely popular, we and others have noticed two potentially undesired behaviours of some ORA tools. The first one we call the 'background problem', because it involves the software eliminating large numbers of genes from the background list if they are not annotated as belonging to any category. The second one we call the 'false discovery rate problem', because some tools underestimate the true number of parallel tests conducted. Results: Here, we demonstrate the impact of these issues on several real RNA-seq datasets and use simulated RNA-seq data to quantify the impact of these problems. We show that the severity of these problems depends on the gene set library, the number of genes in the list, and the degree of noise in the dataset. These problems can be mitigated by changing packages/websites for ORA or by changing to another approach such as functional class scoring. Availability and implementation: An R/Shiny tool has been provided at https://oratool.ziemann-lab.net/ and the supporting materials are available from Zenodo (https://zenodo.org/records/13823301).
Identifiers
What OpenQuestion holds
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.