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Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data

Amit Blumberg, Yixin Zhao, Yi-Fei Huang, Noah Dukler, Edward J. Rice, Alexandra G. Chivu, Katie Krumholz, Charles G. Danko, View ORCID ProfileAdam Siepel
doi: https://doi.org/10.1101/690644
Amit Blumberg
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Yixin Zhao
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Yi-Fei Huang
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Noah Dukler
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Edward J. Rice
2Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
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Alexandra G. Chivu
2Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
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Katie Krumholz
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Charles G. Danko
2Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA
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Adam Siepel
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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  • ORCID record for Adam Siepel
  • For correspondence: asiepel@cshl.edu
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Abstract

The rate at which RNA molecules decay is a key determinant of cellular RNA concentrations, yet current approaches for measuring RNA half-lives are generally labor-intensive, limited in sensitivity, and/or disruptive to normal cellular processes. Here we introduce a simple method for estimating relative RNA half-lives that is based on two standard and widely available high-throughput assays: Precision Run-On and sequencing (PRO-seq) and RNA sequencing (RNA-seq). Our method treats PRO-seq as a measure of transcription rate and RNA-seq as a measure of RNA concentration, and estimates the rate of RNA decay required for a steady-state equilibrium. We show that this approach can be used to assay relative RNA half-lives genome-wide, with good accuracy and sensitivity for both coding and noncoding transcription units. Using a structural equation model (SEM), we test several features of transcription units, nearby DNA sequences, and nearby epigenomic marks for associations with RNA stability after controlling for their effects on transcription. We find that RNA splicing-related features are positively correlated with RNA stability, whereas features related to miRNA binding, DNA methylation, and G+C-richness are negatively correlated with RNA stability. Furthermore, we find that a measure based on U1-binding and polyadenylation sites distinguishes between unstable noncoding and stable coding transcripts but is not predictive of relative stability within the mRNA or lincRNA classes. We also identify several histone modifications that are associated with RNA stability. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.

Competing Interest Statement

The authors have declared no competing interest.

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-ND 4.0 International license.
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Posted June 13, 2020.
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Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data
Amit Blumberg, Yixin Zhao, Yi-Fei Huang, Noah Dukler, Edward J. Rice, Alexandra G. Chivu, Katie Krumholz, Charles G. Danko, Adam Siepel
bioRxiv 690644; doi: https://doi.org/10.1101/690644
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Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data
Amit Blumberg, Yixin Zhao, Yi-Fei Huang, Noah Dukler, Edward J. Rice, Alexandra G. Chivu, Katie Krumholz, Charles G. Danko, Adam Siepel
bioRxiv 690644; doi: https://doi.org/10.1101/690644

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