Direct identification of A-to-I editing sites with nanopore native RNA sequencing.

Inosine is a prevalent RNA modification in animals and is formed when an adenosine is deaminated by the ADAR family of enzymes. Traditionally, inosines are identified indirectly as variants from Illumina RNA-sequencing 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, or in disease contexts where unknown DNA mutations are pervasive. Here, we show that Oxford Nanopore direct RNA sequencing can be used to identify inosine-containing sites in native transcriptomes with high accuracy. We trained convolutional neural network models to distinguish inosine from adenosine and guanosine, and to estimate the modification rate at each editing site. Furthermore, we demonstrated their utility on the transcriptomes of human, mouse and Xenopus. Our approach expands the toolkit for studying adenosine-to-inosine editing and can be further extended to investigate other RNA modifications.

RNA Editing in Human and Mouse Tissues.

Adenosine-to-inosine (A-to-I) RNA editing is a fundamental posttranscriptional mechanism that greatly diversifies the transcriptome in many living organisms, including mammals. Multiple studies have demonstrated the importance of this process not just in normal development and physiology but also in various human diseases. Importantly, the precise editing level of a site may have downstream consequences on cellular behavior. Hence, the editing levels should be quantified as accurately as possible. In this chapter, we describe how to examine RNA editing in human and mouse tissues. The rapid development of next-generation sequencing technologies is affording us an unprecedented ability to accurately measure the editing levels of numerous sites simultaneously. Our experimental workflow includes the harvesting of high-quality RNA samples and the construction of different high-throughput sequencing libraries. We also delineate the computational steps needed to analyze the sequencing data from an Illumina platform.