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02 15, 2021;19 (1): 30.

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
Author Information
  1. Amit Blumberg: Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  2. Yixin Zhao: Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  3. Yi-Fei Huang: Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  4. Noah Dukler: Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  5. Edward J Rice: Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
  6. Alexandra G Chivu: Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
  7. Katie Krumholz: Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  8. Charles G Danko: Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
  9. Adam Siepel: Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. asiepel@cshl.edu. ORCID
PMID: 33588838 DOI: 10.1186/s12915-021-00949-x

Abstract

BACKGROUND: The concentrations of distinct types of RNA in cells result from a dynamic equilibrium between RNA synthesis and decay. Despite the critical importance of RNA decay rates, current approaches for measuring them 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 sequencing (PRO-seq) and RNA sequencing (RNA-seq).
RESULTS: 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 and DNA methylation 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.
CONCLUSION: We introduce an approach for estimating the relative half-lives of individual RNAs. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.

Keywords

Epigenomics PRO-seq RNA half-life RNA splicing Structural equation modeling

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Grants

  1. R01 HG010346/NHGRI NIH HHS
  2. R35-GM127070/NIH HHS
  3. R01-HG009309/NIH HHS
  4. R35 GM127070/NIGMS NIH HHS
  5. R01 HG009309/NHGRI NIH HHS

MeSH Term

Genomic Instability
High-Throughput Nucleotide Sequencing
Humans
RNA Stability
RNA-Seq
Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't
Article has an altmetric score of 11

Word Cloud

Created with Highcharts 10.0.0RNAstabilityrelativePRO-seqtranscriptiondecaymethodhalf-livesRNA-seqmeasurefeaturesconcentrationsequilibriumsensitivitycellularintroduceestimatingbasedsequencingrateapproachgenome-widecodingnoncodingunitsequationseveralnearbyDNAfindcorrelatedbindinganalysisBACKGROUND:distincttypescellsresultdynamicsynthesisDespitecriticalimportanceratescurrentapproachesmeasuringgenerallylabor-intensivelimitedand/ordisruptivenormalprocessessimpletwostandardwidelyavailablehigh-throughputassays:PrecisionRun-OnRESULTS:treatsconcentrationestimatesrequiredsteady-stateshowcanusedassaygoodaccuracyUsingstructuralmodelSEMtestsequencesepigenomicmarksassociationscontrollingeffectssplicing-relatedpositivelywhereasrelatedmiRNAmethylationnegativelyFurthermoreU1polyadenylationsitesdistinguishesunstablestabletranscriptspredictivewithinmRNAlincRNAclassesalsoidentifyhistonemodificationsassociatedCONCLUSION:individualRNAsTogetherestimationsystematicshedlightpervasiveimpactsCharacterizingcombineddataEpigenomicshalf-lifesplicingStructuralmodeling

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