The genetic and biochemical determinants of mRNA degradation rates in mammals
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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 more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms. RESULTS: We establish a compendium of 39 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). The key novel principle learned by Saluki is that the spatial positioning of splice sites, codons, and RNA-binding motifs within an mRNA is strongly associated with mRNA half-life. 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 for transcriptome-wide mRNA half-lives in mammalian cells. Using these revised measurements, we trained Saluki, a model that is over 50% more accurate in predicting half-life from sequence than existing models. 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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Sequence-based models of post-transcriptional regulation rely on genome annotations and transformed measurements extracted from RNA-seq to isolate each regulatory mechanism.
“Similarly, sequence-based models of post-transcriptional regulation rely on genome annotations and transformed measurements extracted from RNA-seq to isolate each regulatory mechanism (for example, percent spliced-in for splicing)14–23.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [23]
The Methods sections on gene annotations, half-life data pre-processing, and Saluki training describe a sequence-based model that uses annotated mRNA structure and transformed half-life measurements to study RNA degradation. The paper does not establish that all those measurements were extracted from RNA-seq, and it models mRNA decay rather than the cited sentence’s splicing example (percent spliced-in). (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe paper did not find sequence elements related to mRNA half-life in Borzoi's sequence attributions.
“Furthermore, we did not find sequence elements related to mRNA half-life in Borzoi’s sequence attributions23,82.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [23]
The Results section, ‘A deep learning-based genetic model of mRNA half-life,’ identifies sequence motifs associated with changes in half-life. It analyzes Saluki, however, and does not report a failure to find those elements in Borzoi’s sequence attributions; the citing authors’ ‘we did not find’ is not a finding of this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrior work has demonstrated strong relationships between codon usage and mRNA half-life.
“Prior work has demonstrated strong relationships between codon usage and mRNA half-life23,78.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [23]
Borzoi performs better than a 3′ UTR-wide ensemble of APARENT2 and Saluki at longer distances (dAUPRC > 0.050 at 2,000 bp) and comparably closer to the PAS (dAUPRC = 0.025 at 50 bp).
“Borzoi performs better at longer distances (dAUPRC > 0.050 at 2,000 bp) with a more comparable performance closer to the PAS (dAUPRC = 0.025 at 50 bp) (Fig. 6g).”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [23]
The Results section on 3′ UTR regulatory function reports Saluki’s performance against experimental data, and its Fig. 6 caption describes panels a–e. Our copy does not address a Borzoi comparison or the stated dAUPRC values at 2,000 bp and 50 bp (searched: Borzoi, APARENT2, dAUPRC, and PAS distance). (LLM, conf=0.78)
Experimental — accuracy not yet measured
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- Authors
- Agarwal, V., & Kelley, D. R.
- Affiliations
- Calico Life Sciences (US)
- Corresponding author
- Agarwal, Vikram
- Kelley, David R.
- Venue
- Genome Biology
- Published
- 2022-11-23
- Volume
- 23
- Issue
- 1
- Pages
- 245-245
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC9684954
- OpenAlex
- W4309925698
- Cited by
- 155
- References
- 111
- 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
- gold
- doi.org
- 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 LLC
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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