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

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Item Type:Review
Title:A comparison of metabolic labeling and statistical methods to infer genome-wide dynamics of RNA turnover
Creators Name:Boileau, E. and Altmüller, J. and Naarmann-de Vries, I.S. and Dieterich, C.
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.
Keywords:Metabolic RNA Labeling, 4sU-Tagging, RNA Decay Rate Estimation, Computational Workflow, Kinetic and Statistical Modeling
Source:Briefings in Bioinformatics
Publisher:Oxford University Press
Page Range:bbab219
Date:November 2021
Official Publication:https://doi.org/10.1093/bib/bbab219
PubMed:View item in PubMed

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