Cargando…

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

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 no...

Descripción completa

Detalles Bibliográficos
Autores principales: Blumberg, Amit, Zhao, Yixin, Huang, Yi-Fei, Dukler, Noah, Rice, Edward J., Chivu, Alexandra G., Krumholz, Katie, Danko, Charles G., Siepel, Adam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885420/
https://www.ncbi.nlm.nih.gov/pubmed/33588838
http://dx.doi.org/10.1186/s12915-021-00949-x
_version_ 1783651601803640832
author Blumberg, Amit
Zhao, Yixin
Huang, Yi-Fei
Dukler, Noah
Rice, Edward J.
Chivu, Alexandra G.
Krumholz, Katie
Danko, Charles G.
Siepel, Adam
author_facet Blumberg, Amit
Zhao, Yixin
Huang, Yi-Fei
Dukler, Noah
Rice, Edward J.
Chivu, Alexandra G.
Krumholz, Katie
Danko, Charles G.
Siepel, Adam
author_sort Blumberg, Amit
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-021-00949-x.
format Online
Article
Text
id pubmed-7885420
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-78854202021-02-17 Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data Blumberg, Amit Zhao, Yixin Huang, Yi-Fei Dukler, Noah Rice, Edward J. Chivu, Alexandra G. Krumholz, Katie Danko, Charles G. Siepel, Adam BMC Biol Methodology Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12915-021-00949-x. BioMed Central 2021-02-15 /pmc/articles/PMC7885420/ /pubmed/33588838 http://dx.doi.org/10.1186/s12915-021-00949-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology Article
Blumberg, Amit
Zhao, Yixin
Huang, Yi-Fei
Dukler, Noah
Rice, Edward J.
Chivu, Alexandra G.
Krumholz, Katie
Danko, Charles G.
Siepel, Adam
Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data
title Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data
title_full Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data
title_fullStr Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data
title_full_unstemmed Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data
title_short Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data
title_sort characterizing rna stability genome-wide through combined analysis of pro-seq and rna-seq data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7885420/
https://www.ncbi.nlm.nih.gov/pubmed/33588838
http://dx.doi.org/10.1186/s12915-021-00949-x
work_keys_str_mv AT blumbergamit characterizingrnastabilitygenomewidethroughcombinedanalysisofproseqandrnaseqdata
AT zhaoyixin characterizingrnastabilitygenomewidethroughcombinedanalysisofproseqandrnaseqdata
AT huangyifei characterizingrnastabilitygenomewidethroughcombinedanalysisofproseqandrnaseqdata
AT duklernoah characterizingrnastabilitygenomewidethroughcombinedanalysisofproseqandrnaseqdata
AT riceedwardj characterizingrnastabilitygenomewidethroughcombinedanalysisofproseqandrnaseqdata
AT chivualexandrag characterizingrnastabilitygenomewidethroughcombinedanalysisofproseqandrnaseqdata
AT krumholzkatie characterizingrnastabilitygenomewidethroughcombinedanalysisofproseqandrnaseqdata
AT dankocharlesg characterizingrnastabilitygenomewidethroughcombinedanalysisofproseqandrnaseqdata
AT siepeladam characterizingrnastabilitygenomewidethroughcombinedanalysisofproseqandrnaseqdata