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Accurate detection of m 6 A RNA modifications in native RNA sequences.

The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N 6 -methyladenosine (m 6 A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m 6 A-modified and unmodified synthetic sequences, can predict m 6 A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m 6 A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these 'errors' are typically not observed in yeast ime4-knockout strains, which lack m 6 A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.

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Accurate detection of m 6 A RNA modifications in native RNA sequences.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

  • 引用：Liu H, Begik O, Lucas MC, Ramirez JM, Mason CE, Wiener D, Schwartz S, Mattick JS, Smith MA, Novoa EM. (2019). Accurate detection of m 6 A RNA modifications in native RNA sequences. Nature communications. PMID 31501426 · PMC6734003 · DOI 10.1038/s41467-019-11713-9
  • 证据类型：PRIMARY_RESEARCH
  • 主题：rna-seq
  • 被引次数（采集时）：447
  • 原始记录：[Europe PMC](https://europepmc.org/article/MED/31501426)
  • 来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

    • 作者摘要（按来源许可复用）

    The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N 6 -methyladenosine (m 6 A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m 6 A-modified and unmodified synthetic sequences, can predict m 6 A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m 6 A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these 'errors' are typically not observed in yeast ime4-knockout strains, which lack m 6 A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.

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    Accurate detection of m 6 A RNA modifications in native RNA sequences. · GeniOmics