On the optimal design of metabolic RNA labeling experiments
Autor: | Isabel S. Naarmann-de Vries, Christoph Dieterich, Alexey Uvarovskii |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
Transcription
Genetic Computer science Molecular biology RNA Stability Biochemistry Sequencing techniques Transcription (biology) Nucleotide Biology (General) Post-Translational Modification RNA Processing Post-Transcriptional RNA transcription labeling chemistry.chemical_classification Ecology Nucleotides Experimental Design High-Throughput Nucleotide Sequencing RNA sequencing Variance (accounting) Asymptotic theory (statistics) Nucleic acids Computational Theory and Mathematics Research Design Modeling and Simulation MCF-7 Cells Metabolic Labeling Algorithm De facto standard Research Article Optimal design QH301-705.5 Nucleic acid synthesis Computational biology Models Biological Deep sequencing Cellular and Molecular Neuroscience Extraction techniques Genetics Humans Biotinylation ddc:610 Chemical synthesis RNA synthesis Massively parallel Ecology Evolution Behavior and Systematics Protocol (science) Biology and life sciences Sequence Analysis RNA RNA Proteins Computational Biology RNA extraction Research and analysis methods Biosynthetic techniques Kinetics Molecular biology techniques chemistry Metabolic labeling Cell Labeling Key (cryptography) Nucleic acid labeling |
Zdroj: | PLoS Computational Biology PLoS Computational Biology, Vol 15, Iss 8, p e1007252 (2019) PLoS Computational Biology 15(8), e1007252 (2019). doi:10.1371/journal.pcbi.1007252 |
ISSN: | 1553-7358 1553-734X |
DOI: | 10.1371/journal.pcbi.1007252 |
Popis: | 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. Author summary 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. In our manuscript, we address several key aspects of experimental design: 1) The optimal labeling time, 2) the number of replicate samples over sequencing depth and 3) the choice of experimental protocol. We provide approximate answers to these questions using asymptotic theory of optimal design. |
Databáze: | OpenAIRE |
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