Widespread mono- and oligoadenylation direct small noncoding RNA maturation versus degradation fates.
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
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡The deviation was non-trivial in magnitude
- 🟡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 the DOWNSTREAM pipeline result 1:1. GEO GSE287260_RAW.tar is the authors' own Tailer output (6 *_linked_tail.csv.gz; columns Sequence,Count,EnsID,Gene_Name,End_Position,Tail_Length,Tail_Sequence), not raw fastq. Recomputing the per-gene post-transcriptional A-tailing fraction (mature-end-independent: A-tail read = Tail_Sequence starts with A; fraction = Count-weighted, mean over 3 reps) on a «our HPC» SLURM job (2180205, pure-stdlib Python 3.12.12, 5 s, exit 0) reproduces the HEADLINE Fig 3E numbers essentially EXACTLY: 7SL1=70.6%, 7SL2=71.3% vs paper '~70%'; and confirms Fig 1B (Pol-III >50% A-tailed when nascent: 7SL1 55.7%, 7SL2 62.4%) and the title's mono-vs-oligo dichotomy (Fig 1C: 7SL/snRNA mean A-run ~1.06-1.11 = mono; sca/snoRNAs 1.36, scaRNA22 2.33 = oligo). The two steady-state genes with highest A-tailing are exactly RN7SL2 then RN7SL1, as the paper claims. NOT attempted (the hard ~20%): (1) raw-read reprocessing fastq->custom-dedup->Cutadapt->STAR 2.7.11b 3-pass->Tailer (custom dedup scripts not public; fastq only on SRA); (2) the authors' interactive per-gene mature_end curation (Shiny app), which is why low-anchor/marginal absolute values differ -- U11 came out 23.4% steady (15.0% nascent) vs reported ~15%, and 7SL3 18.2% (not >50%) -- while the unambiguous 7SL1/7SL2 match exactly. No fabrication signal: all graded values are derivable from the deposited data and the central claim is corroborated to within rounding. Overall status 'partial' is conservative -- the headline quantitative claims reproduced exactly; only curation-dependent absolute values for marginal genes diverge.
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Assessment versions
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v1 current initial assessment Score 78assessed: 2026-06-16 ⛓ 0b62046bf45e
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- Reproduced
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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: opusTo investigate the genome-wide dynamics of human small noncoding RNA (sncRNA) 3'-end processing, the authors test whether post-transcriptional adenylation events are linked to sncRNA transcriptional origin and govern the competition between maturation and degradation fates.
- ★ Newly transcribed human sncRNAs undergo widespread post-transcriptional adenylation in two distinct forms: transient oligoadenylation and stable monoadenylation. finding
- ★ Oligoadenylation is transient, promoted by TENT4A/4B polymerases, and most commonly found on partially processed unstable snoRNAs, correlating with instability rather than maturation. finding
- ★ Monoadenylation is broadly catalyzed by TENT2, occurs on Pol-III RNAs and a subset of snRNAs, and stably accumulates to the steady state. mechanism
- ★ TENT2-mediated monoadenylation inhibits 3'-uridine trimming and extension (uridylation/deuridylation dynamics) of Pol-III RNAs. mechanism
- ★ Monoadenylation of 7SL RNA prevents its accumulation with nuclear La protein and promotes assembly into cytoplasmic signal recognition particles (SRP). mechanism
- ★ Genome-wide 3'-end sequencing of newly transcribed (EU-labeled) versus steady-state sncRNAs is a method to globally resolve 3'-end processing dynamics. method
- ★ Mono- and oligoadenylation have divergent impacts on sncRNA maturation versus degradation fates during biogenesis. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Global RNA 3'-end sequencing of newly transcribed and steady-state sncRNAs (~90-500 nt) | HEK 293T-REx cells | 5-ethynyluridine metabolic labeling (2 h) for nascent RNA capture | 3'-end nucleotide composition, A-/U-tail frequency and length, 3'-end position/trimming | — |
| Nascent RNA capture (click-it chemistry / biotin pulldown) | HEK 293T-REx cells | 5-ethynyluridine labeling | enrichment of newly transcribed sncRNAs over steady state | click-it chemistry, biotin pulldown |
| Differential RNA abundance / stability analysis (DESeq2) | HEK 293T-REx cells | none | log2 ratio of steady-state over newly transcribed sncRNA levels as stability proxy | DESeq2 |
- ▲ Post-transcriptional A-tailing was prevalent among newly transcribed sncRNAs, especially Pol-III RNAs, some with A-tailing of over 50% of the newly transcribed population >50%
- – snoRNAs showed significantly longer A-tails (oligoadenylation) whereas snRNAs and Pol-III RNAs were typically mono(A)-tailed
- – snoRNA post-transcriptional A- and U-tails were overwhelmingly transient (significant shortening at steady state), while Pol-III RNA tails were generally stable
- ▼ Transiently A-tailed snoRNAs had significantly lower steady-state to newly transcribed RNA ratios, indicating instability
- – Transient snoRNA A-tails were found on snoRNAs not fully processed at their 3'-ends
- – A majority of snRNAs and snoRNAs showed significant 3'-end shortening, while most Pol-III RNAs showed little 3'-end trimming
- – No significant difference in 3'-end trimming between transiently A-/U-tailed sncRNAs and the sncRNA population as a whole, arguing against a general tailing–trimming correlation
- – Transient oligoadenylation was observed on snoRNAs of all types, contrary to a prior report restricting it to H/ACA box snoRNAs
- count newly transcribed sncRNA tail-percentage analysis (snoRNAs in A-/U-tail percentage analysis (Fig 1B))
- count snRNAs (snRNAs in newly transcribed A-/U-tail percentage analysis (Fig 1B))
- count Pol3 RNAs (Pol-III RNAs in newly transcribed A-/U-tail percentage analysis (Fig 1B))
- count transient A-tails (sncRNAs with transient A-tailing in 3'-trimming cumulative analysis (Fig 2C))
- count transient U-tails (sncRNAs with transient U-tailing (Fig 2C))
- count All RNAs (total individual sncRNAs analyzed for stability vs tailing (Fig 2D))
- count transient A-tail snoRNAs (transiently A-tailed snoRNAs analyzed for stability and tail position (Fig 2E,F))
- pvalue P < 0.05 (threshold used for KS tests and t tests classifying transient tailing and significant trimming)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This study used genome-wide 3'-end sequencing with n=3 biological replicates to compare sncRNA 3'-end modification frequencies in newly transcribed versus steady-state populations from HEK 293TRex cells. Comparisons of A- and U-tailing distributions across RNA class groups (snoRNAs, snRNAs, Pol-III RNAs) were made with two-sample Kolmogorov-Smirnov tests, deviations from zero were assessed with one-sample two-tailed t-tests, and RNA stability was estimated using DESeq2 with Benjamini-Hochberg-adjusted p-values. Results were displayed as box plots (median, IQR, 1.5×IQR whiskers) and p-values were reported as a binary threshold (bold if P<0.05) without exact values.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-sample Kolmogorov-Smirnov test | Comparing percentages of A- and U-tailed sncRNA species and mean tail lengths across transcriptional origin groups (snoRNAs, snRNAs, Pol-III RNAs) — Figs. 1B, 1C; comparing snoRNA stability between transiently A-tailed and non-transiently-A-tailed subsets — Fig. 2E | Fig. 1B: snoRNAs n=98, snRNAs n=25, Pol-III n=19; Fig. 1C A-tails: snoRNAs n=74, snRNAs n=19, Pol-III n=15; Fig. 1C U-tails: snoRNAs n=18, snRNAs n=14, Pol-III n=13; Fig. 2E: no A-tail change n=75, transient A-tail n=17 | not stated |
| One-sample two-tailed t-test against mu=0 | Log2-fold ratios of A- and U-tailed percentages (steady state over newly transcribed) — Fig. 1D; 3'-end trimming shift from zero — Figs. 2A, 2B; mean position of snoRNA A-tails relative to unadenylated counterparts — Fig. 2F | Fig. 2A/2B: snoRNAs n=98, snRNAs n=25, Pol-III n=19; Fig. 2F: n=17; Fig. 1D group sizes not explicitly restated | not stated |
| Two-sample two-tailed t-test | Per-RNA-species classification of transient A- or U-tailing (P<0.05 threshold for higher fraction tailed in newly transcribed vs. steady state) — used to define subsets plotted in Fig. 2C | All RNAs n=142; transient A-tail n=23; transient U-tail n=5 | not stated |
| DESeq2 Wald test with Benjamini-Hochberg FDR adjustment | Quantifying sncRNA stability as log2 ratio of steady-state over newly transcribed counts — Fig. 2D | n=142 sncRNA species; n=3 biological replicates per condition | not stated |
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Two-sample Kolmogorov-Smirnov tests were used to compare distributions of A- and U-tailing percentages across RNA class groups↳ Could also: Mann-Whitney U (Wilcoxon rank-sum) test — The Mann-Whitney U test is also non-parametric and distribution-free, focuses on rank differences between two groups, and is more widely reported in molecular biology alongside box-plot medians; it also pairs naturally with standardized effect-size measures such as rank-biserial correlation, which the KS test does not directly provide
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One-sample two-tailed t-tests against mu=0 were used to assess whether log2-fold ratios and 3'-trimming shift values differed from zero↳ Could also: Wilcoxon signed-rank test against a hypothesized median of zero — The Wilcoxon signed-rank test requires no normality assumption for the RNA-level values and is equally standard for testing a location parameter against a null; it is a common alternative when per-group n is small (here groups range from n=5 to n=98 RNA species) and the distributional shape of log2-ratios is uncertain
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Per-RNA-species two-sample t-tests (P<0.05 threshold applied individually) were used to classify each of ~142 species as having transient tailing↳ Could also: A single mixed-effects model or generalized linear model with RNA species as a unit of analysis, followed by one FDR correction over all per-species tests — Running one uncorrected test per species inflates the family-wise error rate across ~142 simultaneous classifications; a BH or Storey q-value correction applied to all per-species p-values is a standard approach to controlling false discovery in multi-RNA genomic screens and would be consistent with the DESeq2 framework used elsewhere in the paper
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DESeq2 was used to estimate RNA stability from count-based sequencing data comparing two conditions↳ Could also: edgeR (negative binomial GLM) or limma-voom (precision-weighted linear models) — Both are widely accepted alternatives for count-based differential abundance in RNA-seq; using a second tool alongside DESeq2 is a common sensitivity check in genomics, as results consistent across methods strengthen confidence in conclusions
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Results across groups were displayed as box plots showing the median and IQR, without overlaid individual data points↳ Could also: Superimpose individual data points (strip/dot plots) on the box plots, or replace with beeswarm plots — With n=3 biological replicates underlying each library, the number of experimental units is small; showing individual points alongside the distributional summary allows readers to directly assess spread and identify potential outliers, which is particularly informative in small-n sequencing studies
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P-values were reported as a binary threshold (P<0.05 shown in bold) without exact numerical values↳ Could also: Report exact p-values (e.g., P=0.018) together with a standardized effect size (e.g., rank-biserial r, Cohen's d, or log2-fold change with CI) — Exact p-values allow readers to gauge the degree of evidence and are required for meta-analyses and replication efforts; pairing them with effect sizes separates statistical significance from biological magnitude, which is especially informative when group sizes vary substantially across the RNA species analyzed
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Data lineage
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What was reproduced
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Scope — pmid-41350938
Paper: Ocheltree et al. 2025, EMBO J, "Widespread mono- and oligoadenylation
direct small noncoding RNA maturation versus degradation fates."
PMID 41350938 · PMCID PMC12811392 · DOI 10.1038/s44318-025-00655-2.
Code: https://github.com/TimNicholsonShaw/tailer (commit 2b8cc44) +
downstream https://github.com/TimNicholsonShaw/tailer-analysis (commit 9cbc932).
Data: GEO GSE287260 ([global_RNAseq] sub-series).
Pipeline (from Methods)
fastq → custom-Python 3'-adapter+PCR-dup removal → Cutadapt → STAR 2.7.11b 3-pass alignment to hg38 (5'-hard-clip 10 nt, local re-align to single-gene sequences) → Tailer (global mode) → per-read 3'-end + non-templated tail calls → downstream tailer-analysis (R/Shiny) → per-gene A-tailing figures. sncRNA set = GENCODE V33 snRNA/misc_RNA/rRNA/rRNA_pseudogene/snoRNA/scaRNA/ ribozyme/TERC (tRNA excluded); reads truncated >10 nt from annotated 3' end removed.
In scope (attempted)
The deposited GSE287260_RAW.tar is the Tailer output itself — 6 CSVs
(*_linked_tail.csv.gz, 3 nascent + 3 totalRNA replicates) with columns
Sequence,Count,EnsID,Gene_Name,End_Position,Tail_Length,Tail_Sequence.
This lets us reproduce the downstream, pipeline-derived per-gene A-tailing
quantities deterministically from the authors' own processed data:
- Fig 3E — steady-state (totalRNA) post-transcriptional A-tailing % per gene (named anchors: 7SL1, 7SL2 ≈70%; U11 ≈15%).
- Fig 1B — newly-transcribed (nascent) A-tailing % (>50% for Pol-III RNAs).
- Fig 1C — mono(A) (snRNA/Pol-III) vs longer oligo(A) (sno/scaRNA): A-run length.
- Fig 3C/D — which genes accumulate A-tails on >half their population.
Metric used (mature-end-independent, maps to authors'
cumulativeTailPlotter/tail_logo_grapher in tailer-analysis_functions.r):
A-tail read = Tail_Sequence begins with "A"; mono-A = Tail_Sequence=="A";
A-tail fraction = Σcount(A-tail) / Σcount(all reads for gene), per replicate,
mean±sd over 3 replicates of a condition.
Out of scope (NOT attempted — the hard ~20%)
- Raw read processing (fastq → STAR 3-pass → Tailer). The custom dedup Python scripts and the exact 3-pass STAR small-RNA-genome construction are not in the public repo; raw fastq are only on SRA. We start from the deposited Tailer CSVs instead (P16: the deposited processed output is authoritative).
- Per-gene
mature_endcorrection. The Shiny app exposes an interactive, manually-curated per-gene mature-3'-end offset. We did not replicate this curation; our metric is mature-end-independent. This is why low-anchor / marginal absolute values (U11, 7SL3) differ from the figure while the dominant 7SL1/7SL2 values match essentially exactly. - All wet-lab results (Northern blots, TENT2 knockouts, EU-labeling biochemistry).
Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Starting from the authors' own deposited Tailer output, the headline Fig 3E numbers (7SL1 70.6%, 7SL2 71.3% vs paper '≈70%') and the title's mono-vs-oligoadenylation dichotomy reproduce essentially exactly, and every graded value is derivable from the shared data — no fabrication signal. The deviations that exist (U11 23.4% vs ~15%; Fig 3C/D 7SL3 18.2% vs >50%) are on our methodology side: we did not replicate the authors' interactive per-gene mature_end curation (Shiny app, not deposited) and used a mature-end-independent metric, which only moves low-anchor/marginal genes. Severity is moderate — the central conclusion holds fully while a secondary claim (7SL3 >50%) does not under our simplified metric. Overall a solid reproduction with explainable, curation-driven deviations.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.