Detecting m6A at single-molecular resolution via direct RNA sequencing and realistic training data

• Verfasst von: Chan, Adrian [VerfasserIn]
• Verfasst von: Naarmann-de Vries, Isabel S. [VerfasserIn]
• Verfasst von: Scheitl, Carolin P. M. [VerfasserIn]
• Verfasst von: Höbartner, Claudia [VerfasserIn]
• Verfasst von: Dieterich, Christoph [VerfasserIn]
• Titel: Detecting m6A at single-molecular resolution via direct RNA sequencing and realistic training data
• Verf.angabe: Adrian Chan, Isabel S. Naarmann-de Vries, Carolin P.M. Scheitl, Claudia Höbartner & Christoph Dieterich
• E-Jahr: 2024
• Jahr: 18 April 2024
• Umfang: 8 S.
• Fussnoten: Im Titel ist die Ziffer "6" hochgestellt ; Gesehen am 18.12.2024
• Titel Quelle: Enthalten in: Nature Communications
• Ort Quelle: [London] : Springer Nature, 2010
• Jahr Quelle: 2024
• Band/Heft Quelle: 15(2024), Artikel-ID 3323, Seite 1-8
• ISSN Quelle: 2041-1723
• DOI: doi:10.1038/s41467-024-47661-2
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Machine learning
• Sach-SW: RNA modification
• K10plus-PPN: 1912913143

Abstract

Direct RNA sequencing offers the possibility to simultaneously identify canonical bases and epi-transcriptomic modifications in each single RNA molecule. Thus far, the development of computational methods has been hampered by the lack of biologically realistic training data that carries modification labels at molecular resolution. Here, we report on the synthesis of such samples and the development of a bespoke algorithm, mAFiA (m6A Finding Algorithm), that accurately detects single m6A nucleotides in both synthetic RNAs and natural mRNA on single read level. Our approach uncovers distinct modification patterns in single molecules that would appear identical at the ensemble level. Compared to existing methods, mAFiA also demonstrates improved accuracy in measuring site-level m6A stoichiometry in biological samples.