m6A-SPP: Identification of RNA N6-methyladenosine modification sites through multi-source biological features and a hybrid deep learning architecture

The N6-methyladenosine(m6A) modification plays crucial regulatory roles in various biological processes including gene expression regulation, RNA stability, splicing, and translation. Accurate prediction of m6A modification sites is essential for understanding their biological functions and implications in diseases. To address this, we introduce m6A-SPP, a novel deep learning framework for predicting m6A modification sites effectively. The model integrates both sequence features and physicochemical properties of RNA through two specialized modules. The sequence feature module leverages a pretrained bidirectional encoder representation of transformers (BERT) module (DNABERT), combined with convolutional neural networks (CNN), to provide refined processing of RNA sequence representations. The physicochemical feature module, on the other hand, computes feature embeddings by incorporating three crucial physicochemical properties. The feature matrices from both modules are then concatenated effectively and passed through fully connected layers to produce precise predictions of m6A modification sites. Comprehensive evaluations were performed on a dataset with single-nucleotide resolution for m6A, encompassing eight cell lines (such as HEK293T and HeLa) and three tissue types (including Brain, Liver, and Kidney). The experimental results demonstrate that m6A-SPP surpasses existing methods, highlighting its better performance in predicting m6A modification sites.