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

Adrian Chan, Isabel S. Naarmann- de Vries, Carolin P. M. Scheitl, Claudia Höbartner and Christoph Dieterich ()
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Adrian Chan: University of Heidelberg
Isabel S. Naarmann- de Vries: University of Heidelberg
Carolin P. M. Scheitl: University of Würzburg
Claudia Höbartner: University of Würzburg
Christoph Dieterich: University of Heidelberg

Nature Communications, 2024, vol. 15, issue 1, 1-8

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-47661-2

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