Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 08003, Barcelona, Spain.
Department of Neuroscience, Garvan Institute of Medical Research, Darlinghurst, New South Wales, 2010, Australia.
Nat Commun. 2019 Sep 9;10(1):4079. doi: 10.1038/s41467-019-11713-9.
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-methyladenosine (mA) 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 mA-modified and unmodified synthetic sequences, can predict mA RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify mA 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 mA modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.
在过去的几年中，表观转录组学领域经历了巨大的扩展；然而，主要的限制是缺乏通用的方法来全面绘制 RNA 修饰转录组。在这里，我们表明，通过直接 RNA 测序，可以以系统误差和降低碱基调用质量的形式，以高准确度检测 N6-甲基腺苷（m6A）RNA 修饰。具体来说，我们发现，我们的算法，经过 m6A 修饰和未修饰的合成序列训练，可以以约 90%的准确度预测 m6A RNA 修饰。然后，我们将这些发现扩展到酵母数据集，发现我们的方法可以以 87%的准确度在体内识别 m6A RNA 修饰。此外，我们通过表明这些“错误”通常不会在缺乏 m6A 修饰的酵母 ime4 敲除菌株中观察到，进一步验证了我们的方法。我们的结果为研究 RNA 修饰在其天然 RNA 环境中的生物学作用开辟了途径。
