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

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 is 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. We demonstrate a high inter-method reliability across 8 use case scenarios and offer advice on designing labeling experiments and the respective statistical models. Our results and data will facilitate reproducible research and serve as a resource contributing to a fuller understanding of RNA biology.