Injecting structure-aware insights for the learning of RNA sequence representations to identify m6A modification sites.

ABSTRACT: N6-methyladenosine (m6A) represents one of the most prevalent methylation modifications in eukaryotes and it is crucial to accurately identify its modification sites on RNA sequences. Traditional machine learning based approaches to m6A modification site identification primarily focus on RNA sequence data but often incorporate additional biological domain knowledge and rely on manually crafted features. These methods typically overlook the structural insights inherent in RNA sequences. To address this limitation, we propose M6A-SAI, an advanced predictor for RNA m6A modifications. M6A-SAI leverages a transformer-based deep learning framework to integrate structure-aware insights into sequence representation learning, thereby enhancing the precision of m6A modification site identification.

SUBMITTER: Yu Y 

PROVIDER: S-EPMC11867033 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature