grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis

Teresa Rummel, Lygeri Sakellaridi and Florian Erhard ()
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Teresa Rummel: University of Würzburg
Lygeri Sakellaridi: University of Würzburg
Florian Erhard: University of Würzburg

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. Here we present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.

Date: 2023
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DOI: 10.1038/s41467-023-39163-4

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