Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data
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 disrupti...
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| Format: | Article |
| Language: | English |
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BMC
2021-02-01
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| Series: | BMC Biology |
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| Online Access: | https://doi.org/10.1186/s12915-021-00949-x |
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| author | Amit Blumberg Yixin Zhao Yi-Fei Huang Noah Dukler Edward J. Rice Alexandra G. Chivu Katie Krumholz Charles G. Danko Adam Siepel |
| author_facet | Amit Blumberg Yixin Zhao Yi-Fei Huang Noah Dukler Edward J. Rice Alexandra G. Chivu Katie Krumholz Charles G. Danko Adam Siepel |
| author_sort | Amit Blumberg |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T10:38:21Z |
| format | Article |
| id | doaj.art-402f5011b83a418da6494e4ad564b208 |
| institution | Directory Open Access Journal |
| issn | 1741-7007 |
| language | English |
| last_indexed | 2024-12-14T10:38:21Z |
| publishDate | 2021-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Biology |
| spelling | doaj.art-402f5011b83a418da6494e4ad564b2082022-12-21T23:05:48ZengBMCBMC Biology1741-70072021-02-0119111710.1186/s12915-021-00949-xCharacterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq dataAmit Blumberg0Yixin Zhao1Yi-Fei Huang2Noah Dukler3Edward J. Rice4Alexandra G. Chivu5Katie Krumholz6Charles G. Danko7Adam Siepel8Simons Center for Quantitative Biology, Cold Spring Harbor LaboratorySimons Center for Quantitative Biology, Cold Spring Harbor LaboratorySimons Center for Quantitative Biology, Cold Spring Harbor LaboratorySimons Center for Quantitative Biology, Cold Spring Harbor LaboratoryBaker Institute for Animal Health, College of Veterinary Medicine, Cornell UniversityBaker Institute for Animal Health, College of Veterinary Medicine, Cornell UniversitySimons Center for Quantitative Biology, Cold Spring Harbor LaboratoryBaker Institute for Animal Health, College of Veterinary Medicine, Cornell UniversitySimons Center for Quantitative Biology, Cold Spring Harbor LaboratoryAbstract 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.https://doi.org/10.1186/s12915-021-00949-xRNA half-lifeRNA splicingEpigenomicsPRO-seqStructural equation modeling |
| spellingShingle | Amit Blumberg Yixin Zhao Yi-Fei Huang Noah Dukler Edward J. Rice Alexandra G. Chivu Katie Krumholz Charles G. Danko Adam Siepel Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data BMC Biology RNA half-life RNA splicing Epigenomics PRO-seq Structural equation modeling |
| 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 | RNA half-life RNA splicing Epigenomics PRO-seq Structural equation modeling |
| url | https://doi.org/10.1186/s12915-021-00949-x |
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