Detecting m6A RNA modification from nanopore sequencing using a semisupervised learning framework Article Swipe
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· 2024
· Open Access
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· DOI: https://doi.org/10.1101/gr.278960.124
Direct nanopore-based RNA sequencing can be used to detect posttranscriptional base modifications, such as N6-methyladenosine (m6A) methylation, based on the electric current signals produced by the distinct chemical structures of modified bases. A key challenge is the scarcity of adequate training data with known methylation modifications. We present Xron, a hybrid encoder–decoder framework that delivers a direct methylation-distinguishing basecaller by training on synthetic RNA data and immunoprecipitation (IP)-based experimental data in two steps. First, we generate data with more diverse modification combinations through in silico cross-linking. Second, we use this data set to train an end-to-end neural network basecaller followed by fine-tuning on IP-based experimental data with label smoothing. The trained neural network basecaller outperforms existing methylation detection methods on both read-level and site-level prediction scores. Xron is a standalone, end-to-end m6A-distinguishing basecaller capable of detecting methylated bases directly from raw sequencing signals, enabling de novo methylome assembly.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1101/gr.278960.124
- https://genome.cshlp.org/content/early/2024/10/10/gr.278960.124.full.pdf
- OA Status
- hybrid
- Cited By
- 9
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403415582