Direct mapping of RNA modifications with nanopore sequencing and deep learning
The epitranscriptome, comprising all biochemical modifications on the RNA, dynamically regulates cellular processes. Over 170 RNA modifications, including methylation and deamination, are documented and can affect a myriad of processes such as RNA stability and translation. Despite its importance, the epitranscriptome is incompletely understood due to a technological bottleneck in the mapping of all modifications throughout the transcriptome. Excitingly, nanopore direct RNA sequencing (DRS) has emerged as a promising technology to interrogate the epitranscriptome due to its ability to sequence whole RNA transcripts in their native state. However, the inherently noisy current readout requires specialized computational workflows to accurately pinpoint RNA modifications. Previously, we have developed a method based on convolutional neural networks to detect adenosine-to-inosine (A-to-I) RNA editing. Here, we report the development of an expanded software that can identify multiple RNA modifications on a single read and can provide stoichiometry estimates of each modification at individual sites. Through the predictive capabilities of our software at the single-read level, we conduct in-depth analyses of the interrelationships among various RNA modifications at the single-molecule level. In particular, we explore the effects of NSUN2 and NSUN6 on m5C modifications, shedding light on their intricate interactions. Our set of deep learning models, trained on real biological data generated from wildtype and CRISPR-inactivated cells, surpasses existing approaches and facilitates deeper inquiry into the functions of the epitranscriptome.