Direct identification of A-to-I editing sites in native transcriptomes with nanopore sequencing

Inosine (I) is a prevalent RNA modification in animals and is formed when an adenosine (A) is deaminated by the ADAR family of enzymes. Traditionally, inosines are identified indirectly as variants from Illumina RNA sequencing (RNA-seq) data because they are interpreted as guanosines by cellular machineries. However, this indirect method performs poorly in protein-coding regions where exons are typically short, in non-model organisms with sparsely annotated single nucleotide polymorphisms (SNPs), or in disease contexts like cancer where unknown DNA mutations are pervasive. Here, we show that Oxford Nanopore direct RNA-seq can be used to detect inosines in native RNA transcripts with high accuracy. We trained a convolutional neural network model to distinguish inosine from adenosine and guanosine and demonstrated its utility on the transcriptomes of human, mouse, and Xenopus. Our approach expands the toolkit for the study of A-to-I RNA editing and can be extended to investigate other RNA modifications.