Methyl-GP: accurate generic DNA methylation prediction based on a language model and representation learning

Abstract

Accurate prediction of DNA methylation remains a challenge. Identifying DNA methylation is important for understanding its functions and elucidating its role in gene regulation mechanisms. In this study, we propose Methyl-GP, a general predictor that accurately predicts three types of DNA methylation from DNA sequences. We found that the conservation of sequence patterns among different species contributes to enhancing the generalizability of the model. By fine-tuning a language model on a dataset comprising multiple species with similar sequence patterns and employing a fusion module to integrate embeddings into a high-quality comprehensive representation, Methyl-GP demonstrates satisfactory predictive performance in methylation identification. Experiments on 17 benchmark datasets for three types of DNA methylation (4mC, 5hmC, and 6mA) demonstrate the superiority of Methyl-GP over existing predictors. Furthermore, by utilizing the attention mechanism, we have visualized the sequence patterns learned by the model, which may help us to gain a deeper understanding of methylation patterns across various species.