Detecting m6A RNA modification from nanopore sequencing using a semisupervised learning framework
Abstract
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.
Footnotes
[Supplemental material is available for this article.]
Article published online before print. Article, supplemental material, and publication date are athttps://www.genome.org/cgi/doi/10.1101/gr.278960.124.
Freely available online through theGenome Research Open Access option.
- ReceivedJanuary 17, 2024.
- AcceptedOctober 3, 2024.
This article, published inGenome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described athttp://creativecommons.org/licenses/by-nc/4.0/.
