Correcting 4sU induced quantification bias in nucleotide conversion RNA-seq data

Author:

Berg Kevin12,Lodha Manivel2,Delazer Isabel3,Bartosik Karolina4,Garcia Yilliam Cruz56,Hennig Thomas2,Wolf Elmar56,Dölken Lars2,Lusser Alexandra3ORCID,Prusty Bhupesh K2,Erhard Florian12ORCID

Affiliation:

1. Chair of Computational Immunology, University of Regensburg , Regensburg, Germany

2. Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg , Würzburg, Germany

3. Medical University of Innsbruck, Biocenter, Institute of Molecular Biology , Innsbruck , Austria

4. Institute of Organic Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck , Innsbruck , Austria

5. Cancer Systems Biology Group, Theodor Boveri Institute, University of Würzburg , Würzburg , Germany

6. Institute of Biochemistry, University of Kiel , Kiel , Germany

Abstract

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.

Funder

Bavarian State Ministry of Science and Arts

Deutsche Forschungsgemeinschaft

FOR5200 DEEP-DV

Austrian Science Foundation

University of Regensburg

Publisher

Oxford University Press (OUP)

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