Evidence map›Paper›PMID 38294221›Full record

ArticleMicrobiology spectrum2024

Improving gene set enrichment analysis (GSEA) by using regulation directionality.

Biwen Wang, Frans van der Kloet, Mariah B M J Kes, Joen Luirink, Leendert W Hamoen

Open access · goldAbstract read
In one paragraph

Article in Microbiology spectrum, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. How Bioactive Glass S53P4 Kills Bacteria.Journal of functional biomaterials · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Impact of expression systems on the transcriptome ofApplied and environmental microbiology · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Genomic strategies for drug repurposing.Journal of the Egyptian National Cancer Institute · 2024
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Biwen Wang *Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0003-4383-3902
Frans van der Kloet *Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0002-8573-2651
Mariah B M J KesMolecular Microbiology, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0002-4316-5783
Joen LuirinkMolecular Microbiology, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Leendert W HamoenSwammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0001-9251-1403
University of Amsterdam · NLVrije Universiteit Amsterdam · NL

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) 17833
6 · The paper itself

Abstract

To infer the biological meaning from transcriptome data, it is useful to focus on genes that are regulated by the same regulator, i.e., regulons. Unfortunately, current gene set enrichment analysis (GSEA) tools do not consider whether a gene is activated or repressed by a regulator. This distinction is crucial when analyzing regulons since a regulator can work as an activator of certain genes and as a repressor of other genes, yet both sets of genes belong to the same regulon. Therefore, simply averaging expression differences of the genes of such a regulon will not properly reflect the activity of the regulator. What makes it more complicated is the fact that many genes are regulated by different transcription factors, and current transcriptome analysis tools are unable to indicate which regulator is most likely responsible for the observed expression difference of a gene. To address these challenges, we developed the gene set enrichment analysis program GINtool. Additional features of GINtool are novel graphical representations to facilitate the visualization of gene set analyses of transcriptome data, the possibility to include functional categories as gene sets for analysis, and the option to analyze expression differences within operons, which is useful when analyzing prokaryotic transcriptome and also proteome data.IMPORTANCEMeasuring the activity of all genes in cells is a common way to elucidate the function and regulation of genes. These transcriptome analyses produce large amounts of data since genomes contain thousands of genes. The analysis of these large data sets is challenging. Therefore, we developed a new software tool called GINtool that can facilitate the analysis of transcriptome data by using prior knowledge of gene sets controlled by the same regulator, the so-called regulons. An important novelty of GINtool is that it can take into account the directionality of gene regulation in these analyses, i.e., whether a gene is activated or repressed, which is crucial to assess whether a regulon or functional category is affected. GINtool also includes new graphical methods to facilitate the visual inspection of regulation events in transcriptome data sets. These and additional analysis methods included in GINtool make it a powerful software tool to analyze transcriptome data.

Indexed as

Gene Expression ProfilingTranscriptomeGene Expression Regulation, BacterialOperonSoftwareTranscription FactorsTranscription FactorsBacillus subtilisfunctional categoriesGSEAoperonsregulonstranscriptome analysis

Identifiers

PMID38294221
PMCPMC10913524
OpenAlexW4391403140

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.