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Exploring molecular signatures of senescence with marker, an R Toolkit for evaluating gene sets as phenotypic markers

2026-06Artigo científico Acesso aberto
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dc.contributor.authorMartins-Silva, Rita
dc.contributor.authorKaizeler, Alexandre
dc.contributor.authorBarbosa-Morais, Nuno L.
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - pólo NMS
dc.contributor.pblOxford University Press
dc.date.accessioned2026-06-30T10:28:01Z
dc.date.available2026-06-30T10:28:01Z
dc.date.issued2026-06
dc.descriptionPublisher Copyright: © The Author(s) 2026. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.
dc.description.abstractMany biological processes, including cellular senescence, manifest as diverse phenotypes across cell types and conditions. Lacking definitive markers, researchers often rely on the expression of sets of genes to identify these complex states. However, multiple approaches exist to summarize gene set expression into quantitative metrics (i.e. signatures), each with distinct strengths and limitations, and we know of no consensual framework to systematically evaluate their performance across datasets. We therefore developed markeR, an open-source, modular R package that evaluates gene sets as phenotypic markers using scoring and enrichment-based approaches. markeR generates interpretable metrics and intuitive visualizations for benchmarking gene signatures and exploring their associations with study variables. As a case study, we applied markeR to 9 published senescence-related gene sets across 25 RNA-seq datasets, 6 human cell types and 12 senescence-inducing conditions. Gene set performance varied widely: some signatures (e.g. SenMayo) were robust senescence markers across contexts, while others (e.g. MSigDB sets) performed poorly. We further applied markeR to 49 GTEx tissues, revealing tissue- and age-related differences in senescence-associated signals. Together, these findings emphasize the difficulty of characterizing molecular phenotypes and demonstrate markeR’s potential for the systematic evaluation of gene sets in various biological contexts.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent9282833
dc.identifier.doi10.1093/nargab/lqag057
dc.identifier.issn2631-9268
dc.identifier.otherPURE: 165871923
dc.identifier.otherPURE UUID: 7ad03cda-420e-404a-a2e6-fec7eb95d818
dc.identifier.otherScopus: 105041087603
dc.identifier.urihttp://hdl.handle.net/10362/204190
dc.identifier.urlhttps://www.scopus.com/pages/publications/105041087603
dc.language.isoeng
dc.peerreviewedyes
dc.subjectStructural Biology
dc.subjectMolecular Biology
dc.subjectGenetics
dc.subjectComputer Science Applications
dc.subjectApplied Mathematics
dc.titleExploring molecular signatures of senescence with marker, an R Toolkit for evaluating gene sets as phenotypic markersen
dc.typejournal article
degois.publication.issue2
degois.publication.titleNAR genomics and bioinformatics
degois.publication.volume8
dspace.entity.typePublication
rcaap.rightsopenAccess

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