Evidence map›Paper›PMID 39676751›Full record

ReviewEnvironmental and molecular mutagenesis2025

Outcome of IWGT workshop on transcriptomic biomarkers for genotoxicity: Key considerations for bioinformatics.

Matthew J Meier, Florian Caiment, J Christopher Corton, Roland Frötschl, Yurika Fujita, Danyel Jennen, Roman Mezencev, Carole Yauk

Abstract readReview
In one paragraph

Review in Environmental and molecular mutagenesis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

8 authors.

Matthew J MeierEnvironmental Health, Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada.ORCID 0000-0001-8199-8754
Florian CaimentDepartment of Translational Genomics, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0002-3325-0466
J Christopher CortonCenter for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, North Carolina, USA.ORCID 0000-0002-6197-2036
Roland FrötschlBfArM-Bundesinstitut für Arzneimittel und Medizinprodukte, Federal Institute for Drugs and Medical Devices, Bonn, Germany.ORCID 0000-0001-8721-7509
Yurika FujitaInstitute for Protein Research, Osaka University, Osaka, Japan.ORCID 0000-0002-4350-7229
Danyel JennenDepartment of Translational Genomics, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.ORCID 0000-0002-8618-2487
Roman MezencevCenter for Public Health and Environmental Assessment, Office of Research and Development, US EPA, Washington, DC, USA.
Carole YaukDepartment of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario, Canada.ORCID 0000-0002-6725-3454

Funding

Canada Research Chairs Program CRC-2020-00060Genomics Research and Development InitiativeU.S. Environmental Protection Agency
6 · The paper itself

Abstract

As a part of the International Workshop on Genotoxicity Testing (IWGT) in 2022, a workgroup was formed to evaluate the level of validation and regulatory acceptance of transcriptomic biomarkers that identify genotoxic substances. Several such biomarkers have been developed using various molecular techniques and computational approaches. Within the IWGT workgroup on transcriptomic biomarkers, bioinformatics was a central topic of discussion, focusing on the current approaches used to process the underlying molecular data to distill a reliable predictive signal; that is, a gene set that is indicative of genotoxicity and can then be used in later studies to predict potential DNA damaging properties for uncharacterized chemicals. While early studies used microarray data, a technological shift occurred in the past decade to incorporate modern transcriptome measuring techniques such as high-throughput transcriptomics, which in turn is based on high-throughput sequencing. Herein, we present the workgroup's review of the current bioinformatic approaches to identify genes comprising transcriptomic biomarkers. Within the context of regulatory toxicology, the reproducibility of a given analysis is critical. Therefore, the workgroup provides consensus recommendations on how to facilitate sufficient reporting of experimental parameters for the analytical procedures used in a transcriptomic biomarker study, including the recommendation to develop a biomarker-specific reporting module within the OECD Omics Reporting Framework.

Indexed as

BiomarkersComputational BiologyMutagensTranscriptomeAnimalsDNA DamageGene Expression ProfilingHumansMutagenicity TestsBiomarkersMutagensbioinformaticsbiomarkers of genotoxicityInternational Workshop on Genotoxicity Testingtranscriptomics

Identifiers

PMID39676751
PMCPMC12988029

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.