On the optimal design of metabolic RNA labeling experiments

Autor: Isabel S. Naarmann-de Vries, Christoph Dieterich, Alexey Uvarovskii
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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