Translation affects mRNA stability in a codon-dependent manner in human cells.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough to reproduce 1:1 via a deterministic route. The paper's headline pipeline result is the Codon Stability Coefficient (CSC, Fig 1B) = Pearson correlation, across genes, between each codon's CDS frequency and mRNA stability. I recomputed CSC for all 61 sense codons from the paper's shipped per-gene decay rates (Fig1 source data 1, xlsx) + Ensembl GRCh38 r110 human CDS (longest CDS per gene), and compared to the reported CSC (Fig1 source data 2, csv). Result: HeLa-endo R=0.992, RPE-endo R=0.997, K562-SLAM R=0.999, 293T-endo R=0.919 (244 codon data points total); near-exact except 293T which is noisier. The paper's 'CSC conserved across cell lines' claim (Fig1C) also reproduces (cross-approach R 0.842-0.957 vs reported 0.840-0.960). The small residual (per-codon MAD ~0.005) is explained by using one representative transcript per gene rather than the paper's exact annotation. FABRICATION CHECK: none -- every reported CSC is faithfully derivable from the shipped decay rates; the only quirk is an inconsistent decay_rate sign convention across the 6 sheets (293T sign-inverted vs HeLa/RPE; SLAM stores a raw negative slope), a documentation issue not a data problem. NOT ATTEMPTED (the hard ~20%, per brief): (1) re-running slamdunk v0.3.3 on the raw K562 SLAM-seq fastqs (hg38 + 3'UTR BED + alignment) to regenerate the SLAM half-lives from reads -- heavy plumbing for incremental value once CSC is reproduced; the K562-SLAM CSC was reproduced from the shipped SLAM half-lives instead. (2) ORFome CSC -- clone/ORF ID space not mappable to Ensembl CDS without the ORFome library sequences. Verdict: PARTIAL but strong -- the central codon-dependent-stability result reproduces near-exactly across 4 datasets and 244 data points.
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Assessment versions
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v1 current initial assessment Score 87assessed: 2026-06-14 ⛓ b0bca0958fe3
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusDoes translation affect mRNA stability in a codon-dependent manner (codon optimality) in human cells, and is the regulatory information encoded in codons rather than nucleotides?
- ★ Translation strongly affects mRNA stability in a codon-dependent manner in human cells, with specific codons stabilizing or destabilizing mRNAs. finding
- ★ The regulatory information affecting mRNA stability is encoded in codon identity, not in nucleotide composition. finding
- ★ Stabilizing (optimal) codons tend to be associated with higher tRNA levels and higher charged/total tRNA ratios. mechanism
- ★ mRNAs enriched in destabilizing codons tend to have shorter poly(A)-tails, but the poly(A)-tail is not required for codon-mediated mRNA stability. finding
- ★ The codon-mediated effect depends on translation and is modulated by the number of ribosomes loaded onto an mRNA (e.g. via viral infection or specific UTRs). mechanism
- ★ The codon stability coefficient (CSC) approach quantifies each codon's effect on mRNA stability as the Pearson correlation between codon occurrence and mRNA stability. method
- An ORFome vector-based library (~17,000 coding sequences with shared UTRs/promoter and 3'UTR barcodes) measures coding-sequence regulatory activity independent of UTRs. resource
- Codon optimality codes are conserved across human cell lines, zebrafish, and mouse embryonic stem cells. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Time-course mRNA-seq (endogenous decay profiles) | 293T, HeLa, RPE human cell lines | Actinomycin D (transcription block) | mRNA decay/stability over time, CSC per codon | Actinomycin D 5 μg/ml |
| ORFome decay profiling (barcoded exogenous mRNA-seq) | 293T and K562 cells | Lentiviral ORFome library + Actinomycin D, puromycin selection | Exogenous mRNA decay via 3'UTR barcodes, CSC | ~17,000-vector ORFome library |
| SLAM-seq (metabolic labeling, orthogonal-chemistry RNA-seq) | Human cells incl. K562 | 4-thiouridine (s4U) feed/chase, no transcription block | mRNA half-life via T-to-C conversion, CSC | — |
| 1nt-frameshift fluorescent reporter (qPCR + flow cytometry) | 293T (also HeLa, RPE, K562) | Single-nucleotide frameshift converting optimal to non-optimal CDS; Actinomycin D | Reporter mRNA stability and mCherry/GFP fluorescence intensity | — |
| 'Extreme' premature-stop-codon reporter (qRT-PCR) | Human cells (293T) | Single mutation creating premature stop codon upstream of optimal/non-optimal region; Actinomycin D | Relative reporter mRNA stability | — |
| tRNA level and charged/total tRNA ratio measurement | Human cells | none | tRNA abundance and aminoacylation (charging) ratios vs codon optimality | — |
| Poly(A)-tail length measurement | Human cells | none | Poly(A)-tail length vs codon composition | — |
| m6A target mRNA decay comparison | Human cells (endogenous vs ORFome) | none (predicted m6A targets vs control) | Scaled mRNA decay of m6A targets vs controls | — |
- – Optimal codons show positive correlation and non-optimal codons negative correlation with mRNA stability (CSC) across human cell lines.
- ▲ CSC scores correlate well across 293T, HeLa, RPE human cell types and zebrafish.
- ▲ ORFome-derived CSC scores correlate with endogenous mRNA CSC scores.
- ▼ Endogenous m6A target mRNAs are more unstable than controls, but this difference is largely lost in ORFome-derived mRNAs (lacking 3'UTR elements).
- ▲ SLAM-seq half-lives strongly correlate with TimeLapse-seq in K562 and SLAM-seq CSC correlates with mouse ESC data.
- ▲ Optimal-codon frameshift reporter mRNA is more stable and shows higher mCherry intensity than its non-optimal counterpart, with no change in GFP control.
- ▲ Optimal-codon reporter shows higher RNA level than non-optimal counterpart 24 hr post-transfection (homeostatic effect).
- – Codon-composition effects are independent of frameshift nucleotide identity, fluorescent protein, and coding sequence used.
- count ~17,000 expression vectors/coding sequences (ORFome library size)
- other 20-nucleotide unique barcode (barcode length in 3'UTR of ORFome vectors)
- pvalue ***p<1e-16 (Spearman correlation significance for CSC across cell lines (Fig 1C))
- pvalue *p<1e-50 (Kolmogorov-Smirnov test on mRNA level distribution (Fig 1E))
- pvalue *p<0.05, **p<0.01, ***p<0.005 (Unpaired t-test for reporter stability panels (Fig 2B))
- other s4U fed every 3 hr for 24 hr (SLAM-seq labeling protocol)
- other Cycloheximide 2 μg/ml; Actinomycin D 5 μg/ml (drug concentrations for translation/transcription block)
- count 61 codons (codons scored for stability properties (CSC))
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The study measures codon-dependent mRNA stability in human cells using three independent approaches (Actinomycin D time-course mRNA-seq, an exogenous ORFome reporter library, and SLAM-seq metabolic labeling) across four cell lines. The central metric is the codon stability coefficient (CSC), defined as the Pearson correlation between each codon's occurrence and mRNA stability, and agreement across methods/cell types is assessed with Pearson and Spearman rank correlations. Distributional and group comparisons are reported with Kolmogorov-Smirnov tests, Wilcoxon rank-sum tests, linear models, and unpaired t-tests for reporter experiments, with significance shown as p-value thresholds.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Pearson correlation coefficient (used to compute CSC) | Figure 1A/B — correlation between codon occurrence and mRNA stability; also Figure 1—figure supplement 1F,G,H,I correlations between methods/datasets | — | not stated |
| Spearman rank correlation | Figure 1C and Figure 1—figure supplement 1B,C — correlations of CSC scores across cell lines, methods, and vs codon usage | — | not stated |
| Two-sample Kolmogorov-Smirnov test | Figure 1E (mRNA level distributions, 293T/K562) and Figure 1F (m6A-target vs control mRNA decay) | number of genes (n) indicated in Figure 1F | not stated |
| Linear model | Figure 1F — estimating the difference in scaled mRNA decay between m6A targets and controls | — | not stated |
| Wilcoxon rank-sum test | Figure 1—figure supplement 1D — nucleotide transition ratio with/without s4U treatment | — | not stated |
| Unpaired t-test | Figure 2B reporter decay panels (stated 'Unpaired t.test for all the panels'); related reporter comparisons (2C, 2D) | — | not stated |
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Codon influence on stability was quantified as the CSC using the Pearson correlation between codon occurrence and mRNA stability.↳ Could also: A Spearman rank correlation, or a multiple regression / linear model with all 61 codons entered jointly, could also be used to derive per-codon coefficients. — A rank-based measure is robust to outliers and non-linearity, and a joint regression can account for correlated codon usage across transcripts, which would complement the single-codon Pearson approach.
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Reporter group comparisons were assessed with unpaired t-tests across several panels.↳ Could also: A single ANOVA (or mixed-effects model) with a post-hoc correction, or a non-parametric Mann-Whitney U test, could also be applied. — An ANOVA with post-hoc adjustment controls the family-wise error rate across the multiple related comparisons, while a non-parametric test relaxes the normality assumption that is helpful with small sample sizes.
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Significance was reported using threshold p-values (e.g. p<0.05, p<1e-16).↳ Could also: Reporting exact p-values together with effect sizes and 95% confidence intervals could also be done. — Exact p-values and effect sizes with intervals convey both the magnitude and precision of an effect, which adds interpretive information beyond a significance threshold.
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Distributional differences (e.g. m6A targets vs controls) were evaluated with the Kolmogorov-Smirnov test alongside a linear model.↳ Could also: A Wilcoxon rank-sum test or an explicit effect-size estimate (e.g. difference in medians with a CI) could also summarize the shift. — These approaches directly quantify the location shift and its uncertainty, complementing the KS test which is sensitive to any distributional difference.
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Replicate structure is described in places as triplicates or duplicates without a formal sample-size/power justification.↳ Could also: An a priori power calculation or explicit statement of biological versus technical replication could also be reported. — Documenting the replication basis and expected effect size helps readers interpret the precision of the estimates and the n underlying each test.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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Optimal-codon reporter reaches higher steady-state mRNA levels than non-optimal counterpart, indicating a homeostatic effect.qPCR hek293t up 2019×1papers★ This paper is the founder (earliest)
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Single-nucleotide frameshift to optimal codons increases reporter mRNA stability and protein output relative to non-optimal counterpart.qPCR hek293t up 2019×1papers★ This paper is the founder (earliest)
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Optimal codons correlate positively and non-optimal codons negatively with mRNA stability (CSC) in human cells.RNA-seq human cell-lines mixed 2019×1papers★ This paper is the founder (earliest)
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Exogenous ORFome-derived CSC scores correlate with endogenous mRNA CSC scores.RNA-seq human cell-lines up 2019×1papers★ This paper is the founder (earliest)
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Endogenous m6A-target mRNAs are less stable than controls, an effect lost in ORFome mRNAs lacking 3'UTR elements.RNA-seq human cell-lines down 2019×1papers★ This paper is the founder (earliest)
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CSC scores are reproducible across human cell types and correlate with zebrafish.RNA-seq human zebrafish up 2019×1papers★ This paper is the founder (earliest)
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SLAM-seq codon-based half-lives (no transcription block) correlate with orthogonal labeling and mouse ESC CSC, confirming codon optimality controls stability.RNA-seq k562 up 2019×1papers★ This paper is the founder (earliest)
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-31012849
Paper: Wu Q, Medina SG, Kushawah G, et al. Translation affects mRNA stability in a codon-dependent manner in human cells. eLife 2019;8:e45396. PMID 31012849.
Named code: https://github.com/t-neumann/slamdunk (slamdunk, the SLAM-seq aligner/quantifier; v0.3.3 used by the paper). This is a third-party tool applied to the paper's data — per the brief, equally valid as own-code.
Data: GEO GSE126523 (SuperSeries; SubSeries GSE126520 endogenous decay 293T/HeLa/RPE, GSE126521 ORFome decay, GSE126522 K562 SLAM-seq). SRA PRJNA522258.
Pipeline-derived results (what the paper computes)
- mRNA decay rates / half-lives per gene, by exponential fit of expression (or labeled-fraction for SLAM-seq) over a 0–6 h time course, per cell line / approach. → Figure 1—source data 1 (xlsx, 6 sheets: 293T-endo, HeLa-endo, RPE-endo, 293T-ORFome, k562-ORFome, k562-SLAM-seq).
- Codon Stability Coefficient (CSC) per codon = Pearson correlation, across genes, between a codon's occurrence frequency in the CDS and the mRNA's stability. This is the paper's headline quantitative result ("codon- dependent"). → Figure 1—source data 2 (csv): CSC per codon × approach.
- CSC conservation across cell lines / methods and vs prior data (Fig 1B/1C): CSC vectors correlate strongly across approaches.
IN SCOPE (attempted)
- Recompute CSC (result 2) from the shipped decay rates (result-1 xlsx) + human CDS (Ensembl GRCh38 r110), and compare all 61 sense codons to the reported CSC (Fig 1—source data 2), for the 4 approaches keyed by Ensembl gene IDs: 293T-endo, HeLa-endo, RPE-endo, K562-SLAM. (244 codon data points.)
- Recompute CSC cross-approach conservation (result 3) and compare to the reported-column cross-correlations.
OUT OF SCOPE / not attempted (the hard ~20%, by design)
- Re-running slamdunk on the K562 SLAM-seq fastqs (full map→filter→snp→count on hg38 with a 3′UTR BED) to regenerate the SLAM half-lives from raw reads. Heavy (multi-GB fastq + genome index + annotation plumbing) for incremental value once the headline CSC is reproduced via a deterministic route. The K562-SLAM CSC is still reproduced here (from the shipped SLAM half-lives).
- ORFome CSC (293T-ORFome, k562-ORFome): the ORFome sheets key on a clone/ORF ID space (not Ensembl gene IDs), so the quick CDS-based recompute cannot map them without the ORFome library sequences. Skipped.
- Wet-lab / reporter results (Figs 2–5 source data are oligo/reporter sequences, not pipeline outputs) — out of scope.
Caveat affecting exactness
The paper's exact transcript set per gene is not pinned in the source data; I use one representative CDS per Ensembl gene (longest CDS, Ensembl GRCh38 release-110). This explains the small residual (per-codon MAD ~0.005) vs an exact transcript match; it does not affect the across-codon agreement (R up to 0.999).
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The paper's headline output (the Codon Stability Coefficient, Fig1B) reproduces near-exactly from the authors' shipped decay rates + Ensembl GRCh38 CDS: R=0.992/0.997/0.999 (HeLa/RPE/K562) and 0.919 (293T, noisier), and the CSC-conservation claim (Fig1C) reproduces within ~0.01–0.04. The only deviations sit on the input/preprocessing side and are minor: our choice of one representative transcript per gene (MAD ~0.005, larger 0.042 for 293T) and an inconsistent cross-sheet decay_rate sign convention (a documentation issue, not fabrication). Two items were not attempted (ORFome CSC, re-running slamdunk on raw fastqs), so coverage is partial but the core conclusion holds 1:1.
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Reproduction footprint
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