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image of RMNet: An RNA m6A Cross-species Methylation Detection Method for Nanopore Sequencing

Abstract

Introduction

N6-methyladenosine (m6A) is the most prevalent RNA modification in eukaryotic cells, influencing RNA lifecycle processes. Existing m6A detection methods, such as wet-lab techniques and statistical approaches, are time-consuming, labor-intensive, or require control samples, while machine learning models often lack cross-species applicability. This study aims to develop RMNet, a robust cross-species m6A detection method using nanopore sequencing.

Methods

RMNet employs Conformer and RNN architectures, integrating signal and alignment features from nanopore sequencing data. Contrastive learning enhances differentiation between m6A and non-m6A sites. The model was trained and tested on datasets from synthesized RNA, Arabidopsis, and human samples, using a single set of model weights.

Results

RMNet achieved state-of-the-art performance with accuracies of 99.7% for synthesized RNA, 78.8% for , and 88.9% for human datasets. It outperformed existing methods (m6Anet, DENA, and RedNano) across six metrics, including AUC and AUPR, demonstrating robust cross-species generalization.

Discussion

RMNet’s ability to detect m6A sites across diverse species with a single model addresses limitations of species-specific models. Its high sensitivity and feature representation enable applications in cancer research, neurodevelopmental studies, and plant biology. Limitations include higher error rates in human datasets for thymine-rich k-mers, likely due to complex secondary structures.

Conclusion

RMNet provides an efficient, powerful tool for cross-species m6A detection, advancing epitranscriptomics research with potential applications in precision medicine and agricultural science.

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2025-07-04
2025-09-08

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/content/journals/cdt/10.2174/0113894501405283250627072052
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RMNet: An RNA m6A Cross-species Methylation Detection Method for Nanopore Sequencing
Bentham Science Publishers ; https://doi.org/10.2174/0113894501405283250627072052
/content/journals/cdt/10.2174/0113894501405283250627072052
/content/journals/cdt/10.2174/0113894501405283250627072052
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  • Article Type:     Research Article
Keywords: nanopore sequencing ; plant biology ; conformer ; N6-methyladenosine ; contrastive learning ; RNA

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