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Deep learning identifies A-to-I RNA edits using nanopore sequencing data

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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Fig. 1: A deep learning method to identify inosine-containing sites in native RNA.

References

  1. Eisenberg, E. & Levanon, E. Y. A-to-I RNA editing — immune protector and transcriptome diversifier. Nat Rev. Genet. 19, 473–490 (2018). A Review article that presents an up-to-date summary of A-to-I RNA editing and the ADAR enzymes, which perform the deamination reaction.

    CAS  Article  Google Scholar 

  2. Ramaswami, G. & Li, J. B. Identification of human RNA editing sites: a historical perspective. Methods 107, 42–47 (2016). A Review article that outlines how methods used to identify A-to-I RNA editing sites have progressed since the early days of Sanger sequencing.

    CAS  Article  Google Scholar 

  3. Liu, H. et al. Accurate detection of m6A RNA modifications in native RNA sequences. Nat Commun. 10, 4079 (2019). This paper reports the development of a machine learning method to identify m6A modifications using nanopore direct RNA sequencing.

    Article  Google Scholar 

  4. Begik, O. et al. Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing. Nat. Biotechnol. 39, 1278–1291 (2021). This paper reports the development of a software to detect pseudouridine and 2′-Omethylationin native RNAs using nanopore sequencing.

    CAS  Article  Google Scholar 

  5. Wiener, D. & Schwartz, S. The epitranscriptome beyond m6A. Nat. Rev. Genet. 22, 119–131 (2021). A Review article that presents an up-to-date summary of the universe of RNA modifications.

    CAS  Article  Google Scholar 

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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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