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

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 ide

SUBMITTER: Wu Y 

PROVIDER: S-EPMC11094168 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature