ModiCal : a targeted calibration workflow for site-specific m5C validation by nanopore direct RNA sequencing

dc.contributor.authorÖzrendeci, Zeynep
dc.contributor.authorMündnich, Stefan
dc.contributor.authorPastore, Stefan
dc.contributor.authorWu, Chia Ching
dc.contributor.authorMarchand, Virginie
dc.contributor.authorMotorin, Yuri
dc.contributor.authorRuggieri, Alessia
dc.contributor.authorGerber, Susanne
dc.contributor.authorHelm, Mark
dc.date.accessioned2026-08-05T12:16:30Z
dc.date.issued2026
dc.description.abstractAccurate identification of RNA 5-methylcytidine (m5C) at the single-nucleotide resolution remains a central challenge in nanopore direct RNA sequencing (DRS). Current global scanning and modification-aware basecalling methods enable transcriptome-wide profiling but often yield high false-positive rates and lack site-specific accuracy. To address this, we repurposed ModiDeC, originally a de novo multimodification classifier, into a targeted, high-precision validation tool for RNA modification sites with prior biochemical knowledge. This was implemented through a three-step calibration workflow that alternates between biochemical and computational modules using the well-characterized m5C2278 site in 25S rRNA as a starting point. Baseline training uses short synthetic RNAs carrying either a methylated or unmodified C2278 as ground truth, followed by IVT-derived calibration and validation in methyltransferase knockout yeast. The baseline model accurately detected the bona fide m5C2278 site but initially produced off-target predictions. Iterative retraining with unmodified IVT signals progressively reduced and ultimately eliminated false positives while maintaining a strong signal at the bona fide site. The final model retained enzyme-dependent detection in wild-type versus knockout yeast and, when explicitly targeted, was also able to detect the second rRNA site, C2870, which remained invisible in the initial analysis. Application to native human prerRNA processing intermediates further resolved two distinct m5C deposition regimes on 28S rRNA, while generalization to dengue virus genomic RNA confirmed that the same calibration logic transfers across diverse RNA contexts. Together, this study establishes a reproducible and transferable framework that integrates biochemical validation with iterative neural network refinement, providing a route toward reliable site-specific m5C confirmation by nanopore direct RNA sequencing.en
dc.identifier.doihttps://doi.org/10.25358/openscience-16055
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/16076
dc.language.isoeng
dc.rightsCC-BY-4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc610 Medizinde
dc.subject.ddc610 Medical sciencesen
dc.subject.ddc540 Chemiede
dc.subject.ddc540 Chemistry and allied sciencesen
dc.titleModiCal : a targeted calibration workflow for site-specific m5C validation by nanopore direct RNA sequencingen
dc.typeZeitschriftenaufsatz
jgu.apc.netprice0,00
jgu.apc.price0,00
jgu.apc.taxrate0
jgu.apc.transformationcontractACS
jgu.dfg.year2026
jgu.identifier.uuid4c6cc9c6-78c3-4278-b937-fab636c8c14a
jgu.journal.issue6
jgu.journal.titleACS chemical biology
jgu.journal.volume21
jgu.nationalcurrency.eur0,00
jgu.organisation.departmentFB 09 Chemie, Pharmazie u. Geowissensch.
jgu.organisation.nameJohannes Gutenberg-Universität Mainz
jgu.organisation.number7950
jgu.organisation.placeMainz
jgu.organisation.rorhttps://ror.org/023b0x485
jgu.pages.end1300
jgu.pages.start1291
jgu.publisher.doi10.1021/acschembio.6c00009
jgu.publisher.eissn1554-8937
jgu.publisher.nameACS
jgu.publisher.placeWashington, DC
jgu.publisher.year2026
jgu.rights.accessrightsopenAccess
jgu.subject.ddccode610
jgu.subject.ddccode540
jgu.subject.dfgNaturwissenschaften
jgu.type.dinitypeArticleen_GB
jgu.type.resourceText
jgu.type.versionPublished version

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