YModPred: an interpretable prediction method for multi-type RNA modification sites in S. cerevisiae based on deep learning.

Background: RNA post-transcriptional modifications involve the addition of chemical groups to RNA molecules or alterations to their local structure. These modifications can change RNA base pairing, affect thermal stability, and influence RNA folding, thereby impacting alternative splicing, translation, cellular localization, stability, and interactions with proteins and other molecules. Accurate prediction of RNA modification sites is essential for understanding modification mechanisms.

Results: We propose a novel deep learning model, YModPred, which accurately predicts multiple types of RNA modification sites in S. cerevisiae based on RNA sequences. YModPred combines convolution and self-attention mechanisms to enhance the model's ability to capture global sequence information and improve local feature learning. The model can predict multi-type RNA modification sites. Comparative analysis against benchmark models demonstrates that YModPred outperforms existing state-of-the-art methods in predicting various RNA modification types. Additionally, the model's prediction performance is further validated through visualization and motif analysis.

Conclusions: YModPred is a deep learning-based model that effectively captures sequence features and dependencies, enabling accurate prediction of multi-type RNA modification sites in S. cerevisiae. We believe it will facilitate further research into the mechanisms of RNA modifications.