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Berg K, Lodha M, Delazer I, Bartosik K, Garcia YC, Hennig T, Wolf E, Dölken L, Lusser A, Prusty B, Erhard F. Correcting 4sU induced quantification bias in nucleotide conversion RNA-seq data. Nucleic Acids Res 2024; 52:e35. [PMID: 38381903 PMCID: PMC11039982 DOI: 10.1093/nar/gkae120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 01/25/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
Nucleoside analogues like 4-thiouridine (4sU) are used to metabolically label newly synthesized RNA. Chemical conversion of 4sU before sequencing induces T-to-C mismatches in reads sequenced from labelled RNA, allowing to obtain total and labelled RNA expression profiles from a single sequencing library. Cytotoxicity due to extended periods of labelling or high 4sU concentrations has been described, but the effects of extensive 4sU labelling on expression estimates from nucleotide conversion RNA-seq have not been studied. Here, we performed nucleotide conversion RNA-seq with escalating doses of 4sU with short-term labelling (1h) and over a progressive time course (up to 2h) in different cell lines. With high concentrations or at later time points, expression estimates were biased in an RNA half-life dependent manner. We show that bias arose by a combination of reduced mappability of reads carrying multiple conversions, and a global, unspecific underrepresentation of labelled RNA emerging during library preparation and potentially global reduction of RNA synthesis. We developed a computational tool to rescue unmappable reads, which performed favourably compared to previous read mappers, and a statistical method, which could fully remove remaining bias. All methods developed here are freely available as part of our GRAND-SLAM pipeline and grandR package.
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Affiliation(s)
- Kevin Berg
- Chair of Computational Immunology, University of Regensburg, Regensburg, Germany
- Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Manivel Lodha
- Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Isabel Delazer
- Medical University of Innsbruck, Biocenter, Institute of Molecular Biology, Innsbruck, Austria
| | - Karolina Bartosik
- Institute of Organic Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Yilliam Cruz Garcia
- Cancer Systems Biology Group, Theodor Boveri Institute, University of Würzburg, Würzburg, Germany
- Institute of Biochemistry, University of Kiel, Kiel, Germany
| | - Thomas Hennig
- Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Elmar Wolf
- Cancer Systems Biology Group, Theodor Boveri Institute, University of Würzburg, Würzburg, Germany
- Institute of Biochemistry, University of Kiel, Kiel, Germany
| | - Lars Dölken
- Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Alexandra Lusser
- Medical University of Innsbruck, Biocenter, Institute of Molecular Biology, Innsbruck, Austria
| | - Bhupesh K Prusty
- Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Florian Erhard
- Chair of Computational Immunology, University of Regensburg, Regensburg, Germany
- Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
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