ArticlePLoS computational biology2019
On the optimal design of metabolic RNA labeling experiments.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 22 citations in OpenAlex.
- Spatial mapping of RNA turnover kinetics in the mouse brain.Nature neuroscience · 2026Article
- Spatial mapping of RNA turnover kinetics and regulatory landscapes of mRNA stability in the mammalian brain.bioRxiv : the preprint server for biology · 2026Article
- RNADecayCafe, a uniformly processed atlas of RNA half-life estimates across multiple human cell lines.bioRxiv : the preprint server for biology · 2025Article
- Nanodynamo quantifies subcellular RNA dynamics revealing extensive coupling between steps of the RNA life cycle.Nature communications · 2024Article
- Cellular energy regulates mRNA degradation in a codon-specific manner.Molecular systems biology · 2024Article
- grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis.Nature communications · 2023Article
- Combined modelling of mRNA decay dynamics and single-molecule imaging in the Drosophila embryo uncovers a role for P-bodies in 5' to 3' degradation.PLoS biology · 2023Article
- Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic labeling experiment.BMC bioinformatics · 2022Article
- A comparison of metabolic labeling and statistical methods to infer genome-wide dynamics of RNA turnover.Briefings in bioinformatics · 2021Article
- Dynamics of transcriptional and post-transcriptional regulation.Briefings in bioinformatics · 2021Review
- Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling.Genome research · 2020Article
- A Protocol for Transcriptome-Wide Inference of RNA Metabolic Rates in Mouse Embryonic Stem Cells.Frontiers in cell and developmental biology · 2020Article
- Toward Mapping Spatiotemporally Resolved Transcriptomes and RNA-Protein Interactions.Wiley interdisciplinary reviews. RNAReview
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Massively parallel RNA sequencing (RNA-seq) in combination with metabolic labeling has become the de facto standard approach to study alterations in RNA transcription, processing or decay. Regardless of advances in the experimental protocols and techniques, every experimentalist needs to specify the key aspects of experimental design: For example, which protocol should be used (biochemical separation vs. nucleotide conversion) and what is the optimal labeling time? In this work, we provide approximate answers to these questions using the asymptotic theory of optimal design. Specifically, we investigate, how the variance of degradation rate estimates depends on the time and derive the optimal time for any given degradation rate. Subsequently, we show that an increase in sample numbers should be preferred over an increase in sequencing depth. Lastly, we provide some guidance on use cases when laborious biochemical separation outcompetes recent nucleotide conversion based methods (such as SLAMseq) and show, how inefficient conversion influences the precision of estimates. Code and documentation can be found at https://github.com/dieterich-lab/DesignMetabolicRNAlabeling.
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