Evidence map›Paper›PMID 33588838›Full record

ArticleBMC biology2021

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

Abstract read
In one paragraph

Article in BMC biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. Dissecting thebioRxiv : the preprint server for biology · 2024
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  20. Enhancers associated with unstable RNAs are rare in plants.bioRxiv : the preprint server for biology · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Amit Blumberg *Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Yixin Zhao *Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Yi-Fei HuangSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Noah DuklerSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Edward J RiceBaker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
Alexandra G ChivuBaker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
Katie KrumholzSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Charles G DankoBaker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.
Adam SiepelSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. asiepel@cshl.edu.ORCID 0000-0002-3557-7219

Funding

Evolutionary Human Genomics: Demography, Natural Selection, and Transcriptional RegulationR35GM127070 · NIGMS · COLD SPRING HARBOR LABORATORY · PI Adam Charles Siepel · 2018 to 2026
$4.7M
Evolution of Chromatin Architecture and Transcriptional Regulation in MammalsR01HG010346 · NHGRI · CORNELL UNIVERSITY · PI DANKO, CHARLES GRAHE, SIEPEL, ADAM CHARLES · 2019 to 2022
$2.7M
Mapping RNA polymerase in tissue samples with ChRO-seq.R01HG009309 · NHGRI · CORNELL UNIVERSITY · PI DANKO, CHARLES GRAHE · 2017 to 2021
$1.9M
NHGRI NIH HHS R01 HG009309NHGRI NIH HHS R01 HG010346NIGMS NIH HHS R35 GM127070NIH HHS R01-HG009309NIH HHS R35-GM127070
6 · The paper itself

Abstract

backgroundThe 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).

resultsOur 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.

conclusionWe 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.

Indexed as

Genomic InstabilityRNA StabilityHigh-Throughput Nucleotide SequencingHumansRNA-SeqEpigenomicsPRO-seqRNA half-lifeRNA splicingStructural equation modeling

Identifiers

PMID33588838
PMCPMC7885420

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.