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Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic experiment

Micha Hersch, Adriano Biasini, Ana Claudia Marques, Sven Bergmann
doi: https://doi.org/10.1101/2020.05.01.071779
Micha Hersch
1Department of Computational Biology, University of Lausanne,CH-1015 Lausanne, Switzerland
2Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
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  • For correspondence: micha.hersch@unil.ch
Adriano Biasini
1Department of Computational Biology, University of Lausanne,CH-1015 Lausanne, Switzerland
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Ana Claudia Marques
1Department of Computational Biology, University of Lausanne,CH-1015 Lausanne, Switzerland
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Sven Bergmann
1Department of Computational Biology, University of Lausanne,CH-1015 Lausanne, Switzerland
2Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
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Abstract

Over the past decade, experimental procedures such as metabolic labeling for determining RNA turnover rates at the transcriptome-wide scale have been widely adopted. Several computational methods to estimate RNA processing and degradation rates from such experiments have been suggested, but they all require several RNA sequencing samples. Here we present a method that can estimate RNA synthesis, processing and degradation rates from a single sample. To this end, we use the Zeisel model and take advantage of its analytical solution, reducing the problem to solving a univariate non-linear equation on a bounded domain. This makes our method computationally efficient, while enabling inference of rates that correlate well with previously published data sets. Using our approach on a single sample, we were able to reproduce and extend the observation that dynamic biological processes such as transcription or chromatin modifications tend to involve genes with higher metabolic rates, while stable processes such as basic metabolism involve genes with lower rates.

In addition to saving experimental work and computational time, having a sample-based rate estimation has several advantages. It does not require an error-prone normalization across samples and enables the use of replicates to estimate uncertainty and perform quality control. Finally the method and theoretical results described here are general enough to be useful in other settings such as nucleotide conversion methods.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Added a figure (6) showing that transcript synthesis and degradation rates than can be represented in a frame of reference given by transcript steady-state abundance and responsiveness. The figure also illustrates the fact that some biological processes that are more dynamical involve genes with more responsive transcripts, while more stable biological processes involve genes with less responsive transcripts.

  • https://github.com/BergmannLab/SingleSampleRNAdynamics

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 10, 2020.
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Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic experiment
Micha Hersch, Adriano Biasini, Ana Claudia Marques, Sven Bergmann
bioRxiv 2020.05.01.071779; doi: https://doi.org/10.1101/2020.05.01.071779
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Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic experiment
Micha Hersch, Adriano Biasini, Ana Claudia Marques, Sven Bergmann
bioRxiv 2020.05.01.071779; doi: https://doi.org/10.1101/2020.05.01.071779

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