MultiEditR: The first tool for the detection and quantification of RNA editing from Sanger sequencing demonstrates comparable fidelity to RNA-seq

Mitchell G. Kluesner, Rafail Nikolaos Tasakis, Taga Lerner, Annette Arnold, Sandra Wüst, Marco Binder, Beau R. Webber, Branden S. Moriarity, Riccardo Pecori

Research output: Contribution to journalArticlepeer-review

Abstract

We present MultiEditR (Multiple Edit Deconvolution by Inference of Traces in R), the first algorithm specifically designed to detect and quantify RNA editing from Sanger sequencing (z.umn.edu/multieditr). Although RNA editing is routinely evaluated by measuring the heights of peaks from Sanger sequencing traces, the accuracy and precision of this approach has yet to be evaluated against gold standard next-generation sequencing methods. Through a comprehensive comparison to RNA sequencing (RNA-seq) and amplicon-based deep sequencing, we show that MultiEditR is accurate, precise, and reliable for detecting endogenous and programmable RNA editing.

Original languageEnglish (US)
Pages (from-to)515-523
Number of pages9
JournalMolecular Therapy - Nucleic Acids
Volume25
DOIs
StatePublished - Sep 3 2021

Bibliographical note

Funding Information:
We thank the High Throughput Sequencing unit of the Genomics & Proteomics Core Facility, as well as the Flow Cytometry unit of the Imaging and Cytometry Core Facility, German Cancer Research Center (DKFZ), for providing excellent sequencing and sorting services. We also thank Prof. Nina Papavasiliou (DKFZ), Derek Nedveck, and Walker Lahr for helpful conversations surrounding the topic. This work was supported by the Childrens Cancer Research Fund, the Fanconi Anemia Research Foundation, the University of Minnesota Academic Investment Research Program, and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no. 649019 to Prof. Nina Papavasiliou [DKFZ]). This work was made possible by an NIH-funded predoctoral fellowship to Mitchell G. Kluesner (T32GM007266). The graphical abstract was created with BioRender.com. M.K. R.P. and B.S.M. designed the experiments. M.B. S.W. and T.L. developed KO cell lines. R.P. and A.A. performed titration experiments and RNA base-editing experiments and sequencing. M.K. wrote the program. R.N.T. defined the pipeline for NGS data processing. M.K. R.N.T. and R.P. analyzed the data. M.K. and R.P. wrote the manuscript. M.K. R.P. and B.S.M. supervised the research. All authors have read and approved the manuscript. The authors declare no competing interests.

Funding Information:
We thank the High Throughput Sequencing unit of the Genomics & Proteomics Core Facility, as well as the Flow Cytometry unit of the Imaging and Cytometry Core Facility, German Cancer Research Center (DKFZ), for providing excellent sequencing and sorting services. We also thank Prof. Nina Papavasiliou (DKFZ), Derek Nedveck, and Walker Lahr for helpful conversations surrounding the topic. This work was supported by the Childrens Cancer Research Fund , the Fanconi Anemia Research Foundation , the University of Minnesota Academic Investment Research Program , and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 649019 to Prof. Nina Papavasiliou [DKFZ]). This work was made possible by an NIH-funded predoctoral fellowship to Mitchell G. Kluesner ( T32GM007266 ). The graphical abstract was created with BioRender.com .

Publisher Copyright:
© 2021 The Authors

Keywords

  • ADAR
  • APOBEC
  • DNA base editing
  • Epitranscriptomics
  • MultiEditR Editing Index
  • RNA editing detection
  • RNA editing quantification
  • RNA modification
  • RShiny
  • deaminases

PubMed: MeSH publication types

  • Journal Article

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