Application and prospects of current computational methods in m

N6-methyladenosine (m6A) is the most prevalent modification in eukaryotic mRNA, playing a pivotal role in regulating various aspects of mRNA metabolism, including splicing, processing, degradation, and translation. This review provides a comprehensive overview of computational strategies employed in m6A research, with an emphasis on data-driven methodologies for the prediction of m6A sites and molecular dynamics simulations for deciphering m6A-associated biological mechanisms. The article first discusses the evolution of m6A detection technologies, outlines the corresponding data processing methods, and summarizes publicly available datasets that serve as essential resources for constructing computational models. Subsequently, we highlight research advancements in machine learning and deep learning models for m6A site prediction. Finally, we demonstrate the contributions of molecular dynamics simulations in unravelling m6A-related molecular mechanisms, illustrating how computational methods facilitate the understanding of this complex epigenetic regulation. By systematically synthesizing relevant content, this review further discusses the latest research progress and application values of computational methods in m6A modification, offering new perspectives and insights for in-depth investigations.