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


Journal

Nucleic acids research
ISSN: 1362-4962
Titre abrégé: Nucleic Acids Res
Pays: England
ID NLM: 0411011

Informations de publication

Date de publication:
21 Feb 2024
Historique:
accepted: 07 02 2024
revised: 25 01 2024
received: 23 04 2023
medline: 21 2 2024
pubmed: 21 2 2024
entrez: 21 2 2024
Statut: aheadofprint

Résumé

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.

Identifiants

pubmed: 38381903
pii: 7612100
doi: 10.1093/nar/gkae120
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Bavarian State Ministry of Science and Arts
ID : Bavarian Research Network FOR-COVID
Organisme : Deutsche Forschungsgemeinschaft
ID : ER 927/2-1
Organisme : FOR5200 DEEP-DV
ID : 443644894
Organisme : Austrian Science Foundation
ID : FWF F8009-B
Organisme : University of Regensburg

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.

Auteurs

Kevin Berg (K)

Chair of Computational Immunology, University of Regensburg, Regensburg, Germany.
Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.

Manivel Lodha (M)

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

Isabel Delazer (I)

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

Karolina Bartosik (K)

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

Yilliam Cruz Garcia (YC)

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

Thomas Hennig (T)

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

Elmar Wolf (E)

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

Lars Dölken (L)

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

Alexandra Lusser (A)

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

Bhupesh K Prusty (BK)

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

Florian Erhard (F)

Chair of Computational Immunology, University of Regensburg, Regensburg, Germany.
Institut für Virologie und Immunbiologie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.

Classifications MeSH