Transcriptome-wide analyses of piRNA binding sites suggest distinct mechanisms regulate piRNA binding and silencing in C. elegans.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Any deviation was negligible
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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
REPRODUCED (partial, healthy). RDT (github RyanCCJ/RDT @b8df63c) is the authors' shipped analysis toolkit; it ships the processed per-binding-site/per-read CSVs (data1..5) plus reference PNGs and gene lists that regenerate the paper's read-distribution figures. On «our HPC»/«infra» («job», conda env on compute nodes): 4 reported numbers reproduce EXACTLY -- gene-set sizes Germline 27792 / CSR-1 15821 / WAGO 3644 (line counts of shipped lists) and WS275 transcriptome N=43040 (region-table rows). 4 of 5 figures reproduce within-tol by running RDT on its shipped CSVs and overlaying onto the shipped references: Fig 2-A (miRNA metagene), 2-C (codon), 3-B (predicted-piRNA-site density boxplots), and 4-B-1 (WAGO-1 fold-change; the three M.W.W. p-values reproduce to ~3 significant figures, e.g. 3.318e-289 vs reported 3.324e-289). The plotted curves/boxes are visually IDENTICAL to the references -- the only difference is the auto-title (no --title passed). Fig 5-E-1 is honestly NOT regenerable from shipped data: it overlays two WAGO-9 HRDE-IP datasets (WT1_HRDEIP, CSR_KO1_HRDEIP) absent from shipped data1..5 (uncheckable). Critical env note: repo pins statannot==0.2.3 which needs seaborn<0.12 (sns.categorical._BoxPlotter, removed in seaborn>=0.12); with a modern seaborn the density/fold_change plots crash -- pin seaborn 0.11.2. NOT attempted: re-deriving the CSVs from raw SRA through the external CLASH-analyst/RNAup/pirScan/bowtie upstream (out of scope, external tool). No fabrication signal: every reproduced value/figure is derivable from the shipped data+code.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 88assessed: 2026-06-19 ⛓ 3d64398bb50f
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
- 2026-06-30
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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: sonnetSince C. elegans piRNAs trigger gene silencing only at a select subset of their sequence-predicted targeting sites, the paper asks what cellular mechanisms regulate which predicted piRNA targets are actually bound and silenced in vivo, by comparing in silico predicted piRNA targeting sites to experimentally determined in vivo piRNA binding sites.
- ★ C. elegans piRNAs preferentially bind the coding regions (CDS) of target mRNAs in vivo, rather than 3' UTRs. finding
- ★ Sequence-based predicted piRNA targeting sites are instead enriched at the 3' end/3' UTR of germline mRNAs, opposite to the observed in vivo binding pattern. finding
- ★ piRNA CDS binding preference drives preferential production of secondary WAGO 22G-RNA silencing small RNAs at the CDS. finding
- ★ CSR-1 protects germline mRNAs from piRNA silencing through two distinct mechanisms: inhibiting piRNA binding across the entire CSR-1-targeted transcript, and inhibiting secondary WAGO 22G-RNA production locally at CSR-1-bound sites. mechanism
- ★ The CDS binding preference of piRNAs is not explained by CSR-1 activity, since CDS enrichment of piRNA binding occurs for both CSR-1 (germline-expressed) and WAGO (germline-silenced) targets. finding
- In vivo miRNA (ALG-1) binding sites are enriched at the 3' UTR of mRNAs, confirming the CLASH/iCLIP-based analysis pipeline reproduces known small RNA binding biology. finding
- Germline-enriched miRNAs also remain 3' UTR-enriched even when restricted to germline mRNAs, showing the CDS preference is piRNA-specific and not a general germline small RNA feature. finding
- CLASH (crosslinking, ligation and sequencing of hybrids) analysis of PRG-1 is used as the method to identify direct in vivo piRNA-mRNA binding sites transcriptome-wide. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| CLASH (crosslinking, ligation, sequencing of hybrids) reanalysis of published PRG-1 data | C. elegans germline | none (wild type, reanalysis of published data) | in vivo piRNA binding sites/events on mRNAs | — |
| Bioinformatic prediction of piRNA targeting sites | C. elegans germline mRNAs | none | predicted piRNA targeting site locations and density across 5'UTR/CDS/3'UTR | — |
| iCLIP reanalysis of ALG-1 (miRNA Argonaute) data | C. elegans | none | in vivo miRNA binding site distribution across mRNA regions | — |
| Small RNA sequencing (WAGO-9/HRDE-1 and WAGO-1 22G-RNAs) | C. elegans, WAGO-targeted and CSR-1-targeted germline mRNAs | none | density of WAGO 22G-RNAs across mRNA regions | — |
| Small RNA sequencing, WAGO-1 22G-RNA comparison | C. elegans | prg-1 (PIWI) mutant vs wild type | ratio/reduction of WAGO-1 22G-RNA levels across mRNA regions | — |
| Small RNA sequencing (CSR-1 22G-RNAs) | C. elegans, CSR-1 and WAGO target mRNAs | none | distribution/density of CSR-1 22G-RNAs across gene body | — |
| CLASH reanalysis of PRG-1 binding | C. elegans, CSR-1 and WAGO target mRNAs | CSR-1 depletion vs wild type | piRNA binding events (normalized to piRNA-tRNA hybrid reads) across mRNA regions | — |
- ▲ In vivo piRNA binding density is highest in the CDS of germline mRNAs, peaking near the 5' end and declining sharply after the stop codon.
- ▲ Predicted piRNA targeting sites are enriched at the 3' end, with highest density in the 3' UTR, opposite to observed binding.
- ▲ In vivo miRNA (lin-4/ALG-1) binding sites are located in 3' UTRs, with binding density rising sharply just downstream of the stop codon.
- ▲ WAGO-9 and WAGO-1 22G-RNAs are significantly enriched in the CDS for both WAGO targets and CSR-1 targets.
- ▼ In prg-1 mutants, WAGO-1 22G-RNA levels are reduced more strongly in the CDS than in 5' or 3' UTRs, for both WAGO and CSR-1 targets.
- – CSR-1 22G-RNAs are higher at the 3' end of CSR-1 targets, while CSR-1 22G-RNAs on WAGO targets are more evenly distributed with enrichment at the CDS.
- ▲ Restricting miRNA analysis to germline-enriched miRNAs and germline mRNAs still shows 3' UTR/3' end enrichment, unlike piRNAs.
- – Synthetic piRNAs targeting the CDS trigger robust gene silencing in reporter assays, whereas those targeting 5' or 3' UTRs do not, corroborating the transcriptome-wide CDS preference.
- other Mann-Whitney U-test (Statistical test used to assess significance of density differences between mRNA regions (5' UTR, CDS, 3' UTR) for miRNA binding sites, predicted piRNA targeting sites, in vivo piRNA binding sites, and WAGO/CSR-1 22G-RNA densities across multiple figures; exact P-values not given in the extracted text.)
- other 200 nt window centered at start or stop codon (Window size used for metagene analyses of binding site distribution around start and stop codons for both miRNA and piRNA binding data.)
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.
The paper compares transcriptome-wide sequencing-derived signals (predicted piRNA targeting sites, in vivo piRNA binding sites from CLASH data, miRNA binding sites from iCLIP data, and WAGO/CSR-1 22G-RNA read densities) across mRNA regions (5' UTR, CDS, 3' UTR) and across genotypes (wild type vs. prg-1 or CSR-1-depleted mutants). Results are shown as metagene traces (mean plus/minus one standard error) and as region-wise density comparisons (medians shown as blue lines), with pairwise statistical comparisons performed using the Mann-Whitney U-test. The manuscript text does not include a separate detailed statistical methods section describing sample sizes, replicate numbers, or software versions.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Mann–Whitney U-test | Comparison of miRNA binding site density between mRNA regions (Fig. 2C) | — | not stated |
| Mann–Whitney U-test | Comparison of predicted piRNA targeting site density between mRNA regions (Fig. 3B) | — | not stated |
| Mann–Whitney U-test | Comparison of experimentally identified piRNA binding site density between mRNA regions (Fig. 3D) | — | not stated |
| Mann–Whitney U-test | Comparison of WAGO-9 and WAGO-1 22G-RNA density between mRNA regions, for WAGO and CSR-1 targets (Fig. 4A) | — | not stated |
| Mann–Whitney U-test | Comparison of WAGO-1 22G-RNA fold change (prg-1 mutant/WT) between mRNA regions (Fig. 4B) | — | not stated |
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Densities of binding sites/reads across three mRNA regions (5' UTR, CDS, 3' UTR) were compared using pairwise Mann–Whitney U-tests.↳ Could also: A Kruskal-Wallis test across all three regions followed by a post-hoc test with multiplicity correction (e.g., Dunn's test with Benjamini-Hochberg or Bonferroni adjustment) — This would jointly test for an overall regional difference before pairwise comparisons and would explicitly control the family-wise error rate across the multiple region-to-region comparisons performed throughout the figures.
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Spread in metagene traces is depicted as mean plus/minus one standard error (SE).↳ Could also: Reporting a 95% confidence interval or standard deviation alongside the mean — A confidence interval directly communicates the precision of the estimated mean count at each position, while standard deviation conveys the underlying variability among individual transcripts, which can complement the SE-based depiction.
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Read-density and binding-site comparisons between regions use a nonparametric rank-based test (Mann–Whitney U).↳ Could also: A generalized linear model (e.g., negative binomial regression) modeling counts as a function of region and genotype — Count-based small RNA/CLASH read data are often overdispersed; a negative binomial or Poisson-based regression framework can model region and genotype effects jointly, account for covariates such as transcript length or expression level, and yield effect-size estimates with confidence intervals in addition to significance tests.
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Comparisons of WAGO 22G-RNA levels between wild type and prg-1 mutant animals are summarized as fold-change ratios with density compared by Mann–Whitney U-test.↳ Could also: A paired or repeated-measures analysis (if replicate samples exist) such as a paired Wilcoxon signed-rank test, or a dedicated differential-expression tool (e.g., DESeq2/edgeR) for small RNA count data — If the wild type and mutant measurements derive from matched transcripts or replicate samples, a paired approach or a count-based differential-expression model would account for gene-specific baseline levels and provide shrinkage-based fold-change estimates with associated confidence intervals.
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Sample size, replicate structure (biological vs. technical), and randomization/blinding procedures are not detailed in the text.↳ Could also: Explicit reporting of the number of biological replicates, transcripts, or reads underlying each comparison, along with a power or precision statement — Stating the n underlying each density/metagene comparison would let readers assess the precision of the reported medians and P-values independently.
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Statistical significance across many region-by-region and genotype-by-genotype comparisons is reported without an explicit multiplicity adjustment.↳ Could also: A pre-specified correction such as Benjamini-Hochberg FDR applied across the full set of regional/genotype comparisons in each figure panel — Applying a family-wise or FDR-based correction across the collection of Mann-Whitney comparisons shown in Figures 2-5 would control the overall false-positive rate when many comparisons are presented together.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
scope.md — pmid-36737102
Paper: Wu WS, Brown JS, et al. "Transcriptome-wide analyses of piRNA binding sites suggest distinct mechanisms regulate piRNA binding and silencing in C. elegans." RNA (2023). PMID 36737102 / PMC10158993 / DOI 10.1261/rna.079441.122.
Code (P16, third-party-but-authors' own): https://github.com/RyanCCJ/RDT
— "RNA-seq Read Distribution Toolkit" (RDT), pinned commit b8df63c9220755afc8142a62450e975215dd5875 (2023-10-17, default branch HEAD).
How the analysis is layered
The published analysis has two layers, and the repo ships BOTH plus the processed intermediates:
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Upstream pipeline (
bioinfo/piRNA_project/code/, tool1–tool4):- Input: CLASH/iCLIP hybrids (from external "CLASH analyst"), RNAup energies,
pirScan scores, 22G-RNA bowtie mappings — these RAW-derived inputs are NOT
shipped (the
*_output/dirs contain onlyignore.txtplaceholders). - Produces the per-binding-site CSVs that feed the figures.
- To run this from scratch needs: CLASH analyst (separate tool), RNAup (ViennaRNA), pirScan, bowtie/bowtie2, raw SRA reads. Hard / partially external.
- Input: CLASH/iCLIP hybrids (from external "CLASH analyst"), RNAup energies,
pirScan scores, 22G-RNA bowtie mappings — these RAW-derived inputs are NOT
shipped (the
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Downstream read-distribution + plotting (
rdt-analyze.py,rdt-plot.py,examples/):- Ships the processed per-site/per-read CSVs
examples/data/data1..5.csv(16–65 MB) that directly regenerate the paper's figures, plus reference PNGs inexamples/fig/. - Deterministic pandas/seaborn/matplotlib. Easy, fully self-contained.
- Ships the processed per-site/per-read CSVs
Shipped example data → figure map (from examples/data/README.md + wiki)
| file | sample (SRA) | type | level | figure |
|---|---|---|---|---|
| data1 | AGO/ALG iCLIP (SRR3882949) | RNAup | site | 2-A (metagene), miRNA |
| data2 | PRG-1 CLASH WT1 (SRR6512652) | pirScan | site | piRNA distribution |
| data3 | WAGO-1 IP WT (SRR8482951) | 22G | read_count | 22G |
| data4 | WAGO-1 IP WT, miRNA-normalized | 22G | read_count | fold-change WT arm |
| data5 | WAGO-1 IP prg-1 mutant, miRNA-norm | 22G | read_count | fold-change mutant arm |
Reference figures shipped: examples/fig/{2-A,2-C,3-B,4-B-1,5-E-1}.png.
IN SCOPE (pipeline-derived, attempted)
- S1. Gene-set sizes (Germline 27,792 / CSR-1 15,821 / WAGO 3,644): consistency
check of shipped
gene_list/*vs reported N. DONE offline — all EXACT. - S2. Reference transcriptome N (WS275, 43,040 mRNAs): row count of shipped region table. (verify on «infra»)
- S3. Figure regeneration via RDT downstream (Fig 2-A, 2-C, 3-B, 4-B-1, 5-E-1)
from shipped
data1..5.csv— metagene / density / codon / fold_change. Compare regenerated PNG to shipped reference PNG. Primary reproduction target. - S4 (stretch). Re-derive one
dataN.csvfrom raw reads (e.g. data1 from SRR3882949) through RNAup/CLASH-analyst — upstream layer. Attempt only after S3.
OUT OF SCOPE (not pipeline-derivable here / wet-lab / external tool)
- Wet-lab: PRG-1 CLASH library prep, IP experiments, mutant strains.
- "CLASH analyst" hybrid-calling itself is an external closed-ish tool; not the authors' shipped code. We treat its OUTPUT (shipped CSVs) as the reproduction substrate (valid per brief P16) rather than re-deriving it.
- Biological interpretation / model claims (CSR-1 "local vs transcript-wide inhibition" mechanism) — these are conclusions drawn from the distributions, not separately computable numbers.
Datasets relied upon (for profiling)
SRR3882949 (iCLIP, RU focus), SRR6512652/SRR6512654 (PRG-1 CLASH),
SRR6512642/43 (mRNA-seq), SRR12318132 (CSR-1 22G), SRR8482951/49 (WAGO-1 22G),
SRR12318140/44 (WAGO-9 22G), SRR3356499 (Ribo-seq). Primary profiled accession =
SRR3882949; the shipped data1.csv is its processed binding-site table.
Compute plan
- All on «our HPC»/«infra». Clone RDT + build a small conda env (python, numpy,
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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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.