Adenosine-to-inosine RNA editing is a common post-transcriptional modification, but can be challenging to identify correctly from Illumina data. We show that Oxford Nanopore RNA sequencing, combined with deep learning models, can be used to accurately detect inosine-containing sites in native transcriptomes and to estimate the modification rate of each site.
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References
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This is a summary of: Nguyen, T. A. et al. Direct identification of A-to-I editing sites with nanopore native RNA sequencing. Nat. Methods https://doi.org/10.1038/s41592-022-01513-3 (2022).
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Deep learning identifies A-to-I RNA edits using nanopore sequencing data. Nat Methods 19, 797–798 (2022). https://doi.org/10.1038/s41592-022-01514-2
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DOI: https://doi.org/10.1038/s41592-022-01514-2
