RT Journal Article
SR Electronic
T1 grandR: a comprehensive package for nucleotide conversion sequencing data analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.09.12.507665
DO 10.1101/2022.09.12.507665
A1 Teresa Rummel
A1 Lygeri Sakellaridi
A1 Florian Erhard
YR 2022
UL http://biorxiv.org/content/early/2022/09/15/2022.09.12.507665.abstract
AB 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. We here 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.Competing Interest StatementThe authors have declared no competing interest.