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Systematic calibration of epitranscriptomic maps using a synthetic modification-free RNA library.

Zhang ZhangTao ChenHong-Xuan ChenYing-Yuan XieLi-Qian ChenYu-Li ZhaoBiao-Di LiuLingmei JinWutong ZhangChang LiuDong-Zhao MaGuo-Shi ChaiYing ZhangWen-Shuo ZhaoWen Hui NgJiekai ChenGuifang JiaJianhua YangGuan-Zheng Luo
Published in: Nature methods (2021)
Recent years have witnessed rapid progress in the field of epitranscriptomics. Functional interpretation of the epitranscriptome relies on sequencing technologies that determine the location and stoichiometry of various RNA modifications. However, contradictory results have been reported among studies, bringing the biological impacts of certain RNA modifications into doubt. Here, we develop a synthetic RNA library resembling the endogenous transcriptome but without any RNA modification. By incorporating this modification-free RNA library into established mapping techniques as a negative control, we reveal abundant false positives resulting from sequence bias or RNA structure. After calibration, precise and quantitative mapping expands the understanding of two representative modification types, N6-methyladenosine (m6A) and 5-methylcytosine (m5C). We propose that this approach provides a systematic solution for the calibration of various RNA-modification mappings and holds great promise in epitranscriptomic studies.
Keyphrases
  • nucleic acid
  • single cell
  • machine learning
  • cross sectional
  • low cost
  • quantum dots
  • rna seq
  • deep learning
  • sensitive detection
  • amino acid