Deep-m7G: A contrastive learning-based deep biological language model for identifying RNA N7-methylguanosine sites

Abstract

N7-methylguanosine (m7G) is one of the most prevalent post-transcriptional modifications in RNA molecules, playing a pivotal role in regulating RNA metabolism and function. Given the complexity of canonical m7G cap-dependent protein synthesis, accurately predicting m7G modification sites facilitates further exploration of translation initiation mechanisms. Hence, we collected the most comprehensive single-nucleotide resolution m7G modification sites from the updated m7GHub v2.0 database. We subsequently developed Deep-m7G, a novel contrastive learning-enhanced deep biological language model, designed for both the full transcript and mature RNA datasets. Our methodological framework incorporates three key innovations: (1) implementation of a Most Distant undersampling strategy to mitigate class imbalance in training data; (2) integration of DNABERT-2 with a parallel convolutional neural network architecture for hierarchical feature extraction; and (3) introduction of a contrastive learning module to enhance feature discriminability and model generalizability. Systematic evaluation through 10-fold cross-validation demonstrated the critical contribution of our contrastive learning component. In rigorous benchmarking against existing tools, Deep-m7G achieved superior predictive performance (Full transcript: AUC = 0.960 vs 0.653–0.898 and Mature RNA: AUC = 0.845 vs 0.684–0.832) on independent test sets. Collectively, this computational advance provides a robust framework for the discovery of epitranscriptomics markers, thereby advancing mechanistic investigations of post-transcriptional regulation.