Logo do repositório
 
Publicação

FLYNC: a machine-learning-driven framework for discovering long noncoding RNAs in Drosophila melanogaster.

2026-01Artigo científico Acesso aberto
Ver registo simples
dc.contributor.authorDos Santos, Ricardo F
dc.contributor.authorBaptista, Tiago
dc.contributor.authorMarques, Graça S
dc.contributor.authorHomem, Catarina C F
dc.contributor.institutionNOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
dc.contributor.institutioniNOVA4Health - pólo NMS
dc.contributor.pblOxford University Press
dc.date.accessioned2026-04-09T15:00:02Z
dc.date.available2026-04-09T15:00:02Z
dc.date.issued2026-01
dc.description© The Author(s) 2026. Published by Oxford University Press.
dc.description.abstractNoncoding RNAs have increasingly recognized roles in critical molecular mechanisms of disease. However, the noncoding genome of Drosophila melanogaster, one of the most powerful disease model organisms, has been understudied. Here, we present FLYNC-FLY noncoding RNA discovery and classification-a novel explainable boosting machine model that accurately predicts the probability of a newly identified RNA transcript being a long noncoding RNA (lncRNA). Integrated into an end-to-end bioinformatics pipeline capable of processing single cell or bulk RNA sequencing data, FLYNC outputs potential new noncoding RNA genes. FLYNC leverages large-scale genomic and transcriptomic datasets to identify patterns and features that distinguish noncoding genes from protein-coding genes, thereby facilitating lncRNA prediction. We demonstrate the application of FLYNC to publicly available Drosophila adult head bulk transcriptome and single-cell transcriptomic data from Drosophila neural stem cell lineages and identify several novel tissue- and cell-specific lncRNAs. We have further experimentally validated the existence of a set of FLYNC predicted lncRNAs by RT-PCR and RNA PolII binding. Overall, our findings demonstrate that FLYNC serves as a robust tool for identifying lncRNAs in D. melanogaster, transcending current limitations in ncRNA identification and harnessing the potential of machine learning.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent2825618
dc.identifier.doi10.1093/nargab/lqaf216
dc.identifier.issn2631-9268
dc.identifier.otherPURE: 151154487
dc.identifier.otherPURE UUID: ae82f517-5322-4287-b241-6c10bbc4d9fc
dc.identifier.otherPubMed: 41551930
dc.identifier.otherPubMedCentral: PMC12805895
dc.identifier.otherScopus: 105027977185
dc.identifier.urihttp://hdl.handle.net/10362/202135
dc.language.isoeng
dc.peerreviewedyes
dc.subjectAnimals
dc.subjectDrosophila melanogaster/genetics
dc.subjectRNA, Long Noncoding/genetics
dc.subjectMachine Learning
dc.subjectTranscriptome
dc.subjectSoftware
dc.titleFLYNC: a machine-learning-driven framework for discovering long noncoding RNAs in Drosophila melanogaster.en
dc.typejournal article
degois.publication.firstPage
degois.publication.issue1
degois.publication.lastPage
degois.publication.titleNAR genomics and bioinformatics
degois.publication.volume8
dspace.entity.typePublication
rcaap.rightsopenAccess

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura
Nome:
lqaf216.pdf
Tamanho:
2.69 MB
Formato:
Adobe Portable Document Format
Ver/Abrir

Coleções

NMS: iNOVA4Health - Artigos em revista internacional com arbitragem científica