ArticleFrontiers in cell and developmental biology2021
DeepOMe: A Web Server for the Prediction of 2'-O-Me Sites Based on the Hybrid CNN and BLSTM Architecture.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- OMetaNet: an efficient hybrid deep learning model based on multimodal data fusion and contrastive learning for predicting 2'-O-methylation sites in human RNA.BMC bioinformatics · 2025Article
- 2OMe-LM: predicting 2'-O-methylation sites in human RNA using a pre-trained RNA language model.Bioinformatics (Oxford, England) · 2025Article
- 2OM-Pred: prediction of 2-O-methylation sites in ribonucleic acid using diverse classifiers.Briefings in bioinformatics · 2025Article
- Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI.Frontiers in oncology · 2025Article
- The detection, function, and therapeutic potential of RNA 2'-O-methylation.The innovation life · 2025Article
- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
- MultiV_Nm: a prediction method for 2'-O-methylation sites based on multi-view features.Frontiers in genetics · 2025Article
- Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning.Briefings in bioinformatics · 2024Article
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.International journal of molecular sciences · 2024Article
- Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2'-O-methylation sites in human RNA.PloS one · 2024Article
- H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.Briefings in bioinformatics · 2023Article
- Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy.Frontiers in microbiology · 2023Article
- RPS: a comprehensive database of RNAs involved in liquid-liquid phase separation.Nucleic acids research · 2022Article
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7 authors.
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Abstract
2'-O-methylations (2'-O-Me or Nm) are one of the most important layers of regulatory control over gene expression. With increasing attentions focused on the characteristics, mechanisms and influences of 2'-O-Me, a revolutionary technique termed Nm-seq were established, allowing the identification of precise 2'-O-Me sites in RNA sequences with high sensitivity. However, as the costs and complexities involved with this new method, the large-scale detection and in-depth study of 2'-O-Me is still largely limited. Therefore, the development of a novel computational method to identify 2'-O-Me sites with adequate reliability is urgently needed at the current stage. To address the above issue, we proposed a hybrid deep-learning algorithm named DeepOMe that combined Convolutional Neural Networks (CNN) and Bidirectional Long Short-term Memory (BLSTM) to accurately predict 2'-O-Me sites in human transcriptome. Validating under 4-, 6-, 8-, and 10-fold cross-validation, we confirmed that our proposed model achieved a high performance (AUC close to 0.998 and AUPR close to 0.880). When testing in the independent data set, DeepOMe was substantially superior to NmSEER V2.0. To facilitate the usage of DeepOMe, a user-friendly web-server was constructed, which can be freely accessed at http://deepome.renlab.org.
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