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MultiEditR: The first tool for the detection and quantification of RNA editing from Sanger sequencing demonstrates comparable fidelity to RNA-seq.
Kluesner, Mitchell G; Tasakis, Rafail Nikolaos; Lerner, Taga; Arnold, Annette; Wüst, Sandra; Binder, Marco; Webber, Beau R; Moriarity, Branden S; Pecori, Riccardo.
  • Kluesner MG; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA.
  • Tasakis RN; Center for Genome Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
  • Lerner T; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA.
  • Arnold A; Stem Cell Institute, University of Minnesota, Minneapolis, MN 55455, USA.
  • Wüst S; University of Washington School of Medicine, Seattle, WA 98195, USA.
  • Binder M; Medical Scientist Training Program, University of Washington, Seattle, WA 98195, USA.
  • Webber BR; Division of Immune Diversity, Program in Cancer Immunology, German Cancer Research Centre (DKFZ), 69120 Heidelberg, Germany.
  • Moriarity BS; Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany.
  • Pecori R; Division of Immune Diversity, Program in Cancer Immunology, German Cancer Research Centre (DKFZ), 69120 Heidelberg, Germany.
Mol Ther Nucleic Acids ; 25: 515-523, 2021 Sep 03.
Article en En | MEDLINE | ID: mdl-34589274
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.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Año: 2021 Tipo del documento: Article