A comparison of metabolic labeling and statistical methods to infer genome-wide dynamics of RNA turnover

Author:

Boileau Etienne123,Altmüller Janine456,Naarmann-de Vries Isabel S127,Dieterich Christoph123

Affiliation:

1. Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany

2. Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany

3. DZHK (German Centre for Cardiovascular Research) Partner Site Heidelberg/Mannheim

4. Cologne Center for Genomics (CCG), University of Cologne, Weyertal 115b, 50931, Kön, Germany

5. Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Facility Genomics, Charitéplatz 1, 10117 Berlin, Germany

6. Max Delbrük Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany

7. Department of Intensive Care Medicine, University Hospital Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany

Abstract

Abstract Metabolic labeling of newly transcribed RNAs coupled with RNA-seq is being increasingly used for genome-wide analysis of RNA dynamics. Methods including standard biochemical enrichment and recent nucleotide conversion protocols each require special experimental and computational treatment. Despite their immediate relevance, these technologies have not yet been assessed and benchmarked, and no data are currently available to advance reproducible research and the development of better inference tools. Here, we present a systematic evaluation and comparison of four RNA labeling protocols: 4sU-tagging biochemical enrichment, including spike-in RNA controls, SLAM-seq, TimeLapse-seq and TUC-seq. All protocols are evaluated based on practical considerations, conversion efficiency and wet lab requirements to handle hazardous substances. We also compute decay rate estimates and confidence intervals for each protocol using two alternative statistical frameworks, pulseR and GRAND-SLAM, for over 11 600 human genes and evaluate the underlying computational workflows for their robustness and ease of use. Overall, we demonstrate a high inter-method reliability across eight use case scenarios. Our results and data will facilitate reproducible research and serve as a resource contributing to a fuller understanding of RNA biology.

Funder

Klaus Tschira Stiftung

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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