Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing

You Wu, Wenna Shao, Mengxiao Yan, Yuqin Wang, Pengfei Xu, Guoqiang Huang, Xiaofei Li, Brian D. Gregory, Jun Yang (), Hongxia Wang () and Xiang Yu ()
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You Wu: Shanghai Jiao Tong University
Wenna Shao: Shanghai Jiao Tong University
Mengxiao Yan: Shanghai Chenshan Botanical Garden
Yuqin Wang: Shanghai Chenshan Botanical Garden
Pengfei Xu: Shanghai Jiao Tong University
Guoqiang Huang: Shanghai Jiao Tong University
Xiaofei Li: Shanghai Jiao Tong University
Brian D. Gregory: University of Pennsylvania
Jun Yang: Shanghai Chenshan Botanical Garden
Hongxia Wang: Shanghai Chenshan Botanical Garden
Xiang Yu: Shanghai Jiao Tong University

Nature Communications, 2024, vol. 15, issue 1, 1-19

Abstract: Abstract Nanopore direct RNA sequencing (DRS) has emerged as a powerful tool for RNA modification identification. However, concurrently detecting multiple types of modifications in a single DRS sample remains a challenge. Here, we develop TandemMod, a transferable deep learning framework capable of detecting multiple types of RNA modifications in single DRS data. To train high-performance TandemMod models, we generate in vitro epitranscriptome datasets from cDNA libraries, containing thousands of transcripts labeled with various types of RNA modifications. We validate the performance of TandemMod on both in vitro transcripts and in vivo human cell lines, confirming its high accuracy for profiling m6A and m5C modification sites. Furthermore, we perform transfer learning for identifying other modifications such as m7G, Ψ, and inosine, significantly reducing training data size and running time without compromising performance. Finally, we apply TandemMod to identify 3 types of RNA modifications in rice grown in different environments, demonstrating its applicability across species and conditions. In summary, we provide a resource with ground-truth labels that can serve as benchmark datasets for nanopore-based modification identification methods, and TandemMod for identifying diverse RNA modifications using a single DRS sample.

Date: 2024
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DOI: 10.1038/s41467-024-48437-4

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