 Abstract metabolic labelling 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 modelling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labelling time courses. We demonstrate the need for recalibration of effective labelling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments. This article was authored by Teresa Rommel, Literary Sekelerody, and Florian Earhard.