pulseR: Versatile computational analysis of RNA turnover from metabolic labeling experiments

Uvarovskii, Alexey; Dieterich, Christoph

doi:10.1093/bioinformatics/btx368

Cited by 17 publications

(34 citation statements)

References 9 publications

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“…However, the biochemical separation step is laborious and error-prone, and requires large amounts of RNA. Moreover, imperfect biochemical separation may introduce severe bias and bioinformatic analysis such as data normalization is highly challenging ( Uvarovskii and Dieterich, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Dissecting newly transcribed and old RNA using GRAND-SLAM

2018

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Summary: Global quantification of total RNA is used to investigate steady state levels of gene expression. However, being able to differentiate pre-existing RNA (that has been synthesized prior to a defined point in time) and newly transcribed RNA can provide invaluable information e.g. to estimate RNA half-lives or identify fast and complex regulatory processes. Recently, new techniques based on metabolic labeling and RNA-seq have emerged that allow to quantify new and old RNA: Nucleoside analogs are incorporated into newly transcribed RNA and are made detectable as point mutations in mapped reads. However, relatively infrequent incorporation events and significant sequencing error rates make the differentiation between old and new RNA a highly challenging task. We developed a statistical approach termed GRAND-SLAM that, for the first time, allows to estimate the proportion of old and new RNA in such an experiment. Uncertainty in the estimates is quantified in a Bayesian framework. Simulation experiments show our approach to be unbiased and highly accurate. Furthermore, we analyze how uncertainty in the proportion translates into uncertainty in estimating RNA half-lives and give guidelines for planning experiments. Finally, we demonstrate that our estimates of RNA half-lives compare favorably to other experimental approaches and that biological processes affecting RNA half-lives can be investigated with greater power than offered by any other method. GRAND-SLAM is freely available for non-commercial use at http://software.erhard-lab.de; R scripts to generate all figures are available at zenodo (doi: 10.5281/zenodo.1162340).

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Section: Introductionmentioning

confidence: 99%

Dissecting newly transcribed and old RNA using GRAND-SLAM

2018

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“…Alternatively, if the modified nucleotides are chemically derivatized before sequencing, reads from nascent transcripts can be in silico separated from pre-existing RNA (Herzog et al 2017;Baptista and Dölken 2018;Jürges et al 2018;Schofield et al 2018). A number of methods were developed for the quantification of RNA dynamics via metabolic labeling, including INSPEcT (de Pretis et al 2015), DRUID (Lugowski et al 2018), cDTA (Sun et al 2012), GRAND-SLAM (Jürges et al 2018), pulseR (Uvarovskii and Dieterich 2017), and DRiLL (Rabani et al 2014). Eventually, these approaches have started to unveil how the modulation of RNA dynamics can determine gene-specific regulatory modes and elicit complex transcriptional responses (Rabani et al 2014;de Pretis et al 2015de Pretis et al , 2017Furlan et al 2019;Tesi et al 2019).…”

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confidence: 99%

Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling

et al. 2020

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The quantification of the kinetic rates of RNA synthesis, processing, and degradation are largely based on the integrative analysis of total and nascent transcription, the latter being quantified through RNA metabolic labeling. We developed INSPEcT−, a computational method based on the mathematical modeling of premature and mature RNA expression that is able to quantify kinetic rates from steady-state or time course total RNA-seq data without requiring any information on nascent transcripts. Our approach outperforms available solutions, closely recapitulates the kinetic rates obtained through RNA metabolic labeling, improves the ability to detect changes in transcript half-lives, reduces the cost and complexity of the experiments, and can be adopted to study experimental conditions in which nascent transcription cannot be readily profiled. Finally, we applied INSPEcT− to the characterization of post-transcriptional regulation landscapes in dozens of physiological and disease conditions. This approach was included in the INSPEcT Bioconductor package, which can now unveil RNA dynamics from steady-state or time course data, with or without the profiling of nascent RNA.

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“…Note that the right-hand side of (22) is in general continuous in k = 1, but not in k = 1 − a. Furthermore, it can be shown (see Appendix 2) that for b > 1 2−a , (22) has a single solution in the domain given by (24), which can be found very efficiently. This enables the estimation of the processing and degradation rates for a single sample.…”

Section: Inferring Processing and Degradation Ratesmentioning

confidence: 99%

“…Those equations are then reparametrized with dimensionless parameters and reduced to a single non-linear equation with one unknown (22). This resulting equation is only defined on a bounded domain (24). Our rates can thus be inferred by numerically solving that equation on a bounded domain, which is very fast.…”

Section: Introductionmentioning

confidence: 99%

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

Hersch

Biasini

Marques

et al. 2020

Preprint

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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 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. The approach is computationnaly rapid and enables inference of rates that correlate well with previously published datasets. 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.

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pulseR: Versatile computational analysis of RNA turnover from metabolic labeling experiments

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 17 publications

References 9 publications

Dissecting newly transcribed and old RNA using GRAND-SLAM

Dissecting newly transcribed and old RNA using GRAND-SLAM

Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling

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

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