2OMe-LM: predicting 2'-O-methylation sites in human RNA using a pre-trained RNA language model

2'-O-methylation (2OMe) is a common post-transcriptional modification in RNA that plays a crucial role in regulating gene expression and is implicated in various biological processes and diseases. Computational methods offer an efficient alternative to the time-consuming and costly experimental identification of 2OMe sites. Recent advancements in RNA pre-trained language models have revolutionized RNA bioinformatics. However, there remains a gap in their application specifically for predicting 2OMe sites.

In the study, we propose a novel deep learning framework, 2OMe-LM, for predicting 2OMe sites in RNA. 2OMe-LM integrates RNA sequence features derived from RNA pre-trained language models with those obtained from the word2vec technique. Then, 2OMe-LM employs fully connected layers and a bidirectional long short-term memory network to process the two types of features separately, followed by a feature fusion module for the final prediction. Additionally, an attention block is incorporated to provide the interpretability of the prediction results. The results demonstrate that 2OMe-LM significantly outperforms existing state-of-the-art predictors, with features from RNA pre-trained language models proving to be critical. Motif analysis further demonstrates 2OMe-LM's potential for discovering 2OMe-related motifs.

The 2OMe-LM web server is available at https://csuligroup.com:9200/2OMe-LM. The source code can be obtained from https://github.com/CSUBioGroup/2OMe-LM.