RNA m6A detection using raw current signals and basecalling errors from nanopore direct RNA sequencing reads.

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Abstract

Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory technique. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling error features, ignoring the richer information of the raw signals of DRS reads.Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher AUCs and AUPRs in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of P. trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8-9.9% and 5.5-13.8% higher than other methods, respectively.The source code of RedNano is freely available at https://github.com/Derryxu/RedNano.Supplementary data are available at Bioinformatics online.© The Author(s) 2024. Published by Oxford University Press.