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PreprintCited by 155RNA Research and SplicingRNA and protein synthesis mechanismsRNA regulation and disease

The genetic and biochemical determinants of mRNA degradation rates in mammals

Open access
Agarwal, Vikram, Kelley, David·2022·Genome biology
DOI 10.1101/2022.03.18.484474PMC PMC9684954

Preprint — published as 10.1186/s13059-022-02811-x · Paper page

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Abstract

Background: Degradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable a more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms. Results: Here, we establish a compendium of 54 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays. Conclusions: Our work produces a more robust “ground truth” with regards to transcriptome-wide mRNA half-lives in mammalian cells. Using these consolidated measurements, we trained a model that is over 50% more accurate in predicting half-life from sequence than existing models. Our best model, Saluki, succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome.

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Authors
Agarwal, V., & Kelley, D.
Affiliations
Calico Life Sciences (US)
Corresponding author
  • Agarwal, Vikram
  • Kelley, David
ORCID
Venue
Genome biology
Publisher
openRxiv
Published
2022-03-19
Volume
23
Issue
1
Pages
245-245
ISSN
1465-6906 (ISSN-L)
DOI
10.1101/2022.03.18.484474
PMC
PMC9684954
OpenAlex
W4309925698
Cited by
155
References
1
Citations by year
2026: 38 · 2025: 57 · 2024: 36 · 2023: 18 · 2022: 5 · 2020: 1
FWCI
3.02
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Calico Life Sciences
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
Keywords
mRNA half-life, mRNA degradation rate, mRNA stability, post-transcriptional regulation, deep neural network inference, splice sites, RNA-binding motif, codon position, measurement bias correction, massively parallel reporter assay
MeSH terms
Animals, Biological Assay, Humans, Mammals, RNA, Messenger, RNA Stability, Mice, Transcriptome

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