Accurate detection of m6A RNA modifications in native RNA sequences

The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N6-methyladenosine (m6A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m6A-modified and unmodified synthetic sequences, can predict m6A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m6A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these ‘errors’ are typically not observed in yeast ime4-knockout strains, which lack m6A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.

'The establishment of the [Oxford Nanopore] platform as a tool to map virtually any given modification will allow us to query the epitranscriptome in ways that, until now, had not been possible'

Liu et al

Authors: Huanle Liu, Oguzhan Begik, Morghan C Lucas, Christopher E Mason, Schraga Schwartz, John S Mattick, Martin A Smith, Eva Maria Novoa