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

doi:10.1016/j.omtn.2021.07.008

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

Pediatric Hematology/Oncology

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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 language	English (US)
Pages (from-to)	515-523
Number of pages	9
Journal	Molecular Therapy - Nucleic Acids
Volume	25
DOIs	https://doi.org/10.1016/j.omtn.2021.07.008
State	Published - 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

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10.1016/j.omtn.2021.07.008

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Kluesner, M. G., Tasakis, R. N., Lerner, T., Arnold, A., Wüst, S., Binder, M., Webber, B. R., Moriarity, B. S., & Pecori, R. (2021). MultiEditR: The first tool for the detection and quantification of RNA editing from Sanger sequencing demonstrates comparable fidelity to RNA-seq. Molecular Therapy - Nucleic Acids, 25, 515-523. https://doi.org/10.1016/j.omtn.2021.07.008

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

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.",

keywords = "ADAR, APOBEC, DNA base editing, Epitranscriptomics, MultiEditR Editing Index, RNA editing detection, RNA editing quantification, RNA modification, RShiny, deaminases",

author = "Kluesner, {Mitchell G.} and Tasakis, {Rafail Nikolaos} and Taga Lerner and Annette Arnold and Sandra W{\"u}st and Marco Binder and Webber, {Beau R.} and Moriarity, {Branden S.} and Riccardo Pecori",

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{\textquoteright}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: {\textcopyright} 2021 The Authors",

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doi = "10.1016/j.omtn.2021.07.008",

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T2 - The first tool for the detection and quantification of RNA editing from Sanger sequencing demonstrates comparable fidelity to RNA-seq

AU - Kluesner, Mitchell G.

AU - Tasakis, Rafail Nikolaos

AU - Lerner, Taga

AU - Arnold, Annette

AU - Wüst, Sandra

AU - Binder, Marco

AU - Webber, Beau R.

AU - Moriarity, Branden S.

AU - Pecori, Riccardo

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PY - 2021/9/3

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N2 - 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.

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KW - APOBEC

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KW - RNA editing detection

KW - RNA editing quantification

KW - RNA modification

KW - RShiny

KW - deaminases

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ER -

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

Abstract

Bibliographical note

Keywords

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