2-AIN-505, 2-AIN-251: Seminar in Bioinformatics (1), (3)
Winter 2024
Abstrakt

You Wu, Wenna Shao, Mengxiao Yan, Yuqin Wang, Pengfei Xu, Guoqiang Huang, Xiaofei Li, Brian D. Gregory, Jun Yang, Hongxia Wang, Xiang Yu. Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing. Nat Commun, 15(1):4049. 2024.

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Download from publisher: https://www.nature.com/articles/s41467-024-48437-4 PubMed

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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 m(6)A and m(5)C modification sites. Furthermore, we perform transfer 
learning for identifying other modifications such as m(7)G, Psi, 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.