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Updated: Jun 28, 2026
Identification of Circular RNAs using RNA Sequencing
Published on: November 14, 2019
BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs
Yuqian Yuan1, Xiaozhu Tang2, Hongyan Li1
1School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China.
Abstract:
m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications, a critical gap remains in the prediction of circRNA m6A modifications. Therefore, accurate identification and prediction of m6A sites are imperative for understanding RNA function and regulation. This study presents a novel hybrid model combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM) for precise m6A methylation site prediction in circular RNAs (circRNAs) based on data from HEK293 cells. This model exploits the synergy between CNN's ability to extract intricate sequence features and BiLSTM's strength in capturing long-range dependencies. Furthermore, the integrated attention mechanism empowers the model to pinpoint critical biological information for studying circRNA m6A methylation. Our model, exhibiting over 78% prediction accuracy on independent datasets, offers not only a valuable tool for scientific research but also a strong foundation for future biomedical applications. This work not only furthers our understanding of gene expression regulation but also opens new avenues for the exploration of circRNA methylation in biological research.
Insights
Researchers developed a new AI model to predict m6A methylation sites on circular RNAs (circRNAs). This tool accurately identifies methylation patterns, advancing our understanding of gene expression and disease.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genomics
Background:
- N6-methyladenosine (m6A) methylation is a crucial RNA modification impacting gene expression and cellular processes.
- While m6A prediction models exist for messenger RNA (mRNA), a gap persists for circular RNAs (circRNAs).
- Accurate prediction of m6A sites on circRNAs is vital for understanding their regulatory roles and involvement in diseases.
Purpose of the Study:
- To develop a novel computational model for precise prediction of m6A methylation sites in circRNAs.
- To leverage the strengths of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks for enhanced prediction accuracy.
Main Methods:
- A hybrid CNN-BiLSTM model was designed, integrating an attention mechanism.
- The model was trained and validated using m6A methylation data from HEK293 cells.
- Feature extraction capabilities of CNNs and long-range dependency handling of BiLSTMs were synergistically employed.
Main Results:
- The developed model achieved over 78% prediction accuracy on independent datasets.
- The attention mechanism effectively highlighted critical biological information for circRNA m6A methylation.
- The model demonstrated superior performance in identifying m6A sites within circRNA sequences.
Conclusions:
- The novel CNN-BiLSTM hybrid model provides a valuable tool for accurate circRNA m6A site prediction.
- This advancement enhances the understanding of circRNA function and regulation in biological systems.
- The findings lay a foundation for future biomedical applications targeting circRNA methylation.
