Contribution of retrotransposition to developmental disorders.
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.
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡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 — YES, and reproduced 1:1 on the parts that are public. NOTE: the scaffold's auto-enriched links were both false positives (code 'PacBio/unanimity' and data 'GSE74432' are unrelated); the real artifacts are the authors' own repos github.com/eugenegardner/MEIPaper (@0e709a3) + Retrogene (@d65b170), with primary data in EGA controlled access (EGAS00001000775). The raw exomes/BAMs are restricted, so MELT/Retrogene variant CALLING could not be re-run (a real, declared blocker). HOWEVER the MEIPaper repo ships the complete downstream R analysis together with its input summary tables, and that pipeline reproduces the paper's headline numbers EXACTLY on «our HPC»: all six Table 1 per-individual MEI/PPG means+SD (Alu 23.61±4.25 vs 23.6±4.2, total 26.57±4.68 vs 26.6±4.7, etc.), both Table 2 germline mutation rates (1.21e-11 DDD, 1.40e-11 1KGP), the '1 new MEI per 12-14 births' genome-wide rate (12.42-14.39), and the coin-flip P=1.7e-5 (~10^-5 as stated). Supplementary statistics (1KGP downsampling z-tests, mosaic Wilcoxon, PPG-vs-Zhang regression R2=0.64, pLI Fisher, strand chisq) also recomputed deterministically and recorded. The 1-in-2434 (0.04%) diagnostic figure is arithmetically confirmed (4/9738=1/2434.5=0.041%). NOT ATTEMPTED (out of scope): MELT/Retrogene calling from restricted EGA data, the 9-de-novo and 4-causative counts (need restricted data + manual clinical review), wet-lab Figure 3 PCR/blots. Verdict: faithful 1:1 reproduction of the entire shipped (downstream) pipeline; no fabrication signal — every public claim is derivable from the shipped data+code. Honest partial because upstream calling is gated by controlled-access data, not by any repo/doc deficiency.
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 79assessed: 2026-06-16 ⛓ 62a585393737
✎ 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
- 2026-06-16
- 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: opusThe paper tests whether and at what rate de novo retrotransposition (RT) events—mobile element insertions (MEIs) and processed pseudogenes (PPGs)—contribute to severe developmental disorders, and seeks to estimate genome-wide germline ME mutation rate and selective constraint.
- ★ De novo retrotransposition events cause a small but detectable fraction of severe developmental disorders, with 4 of 9 de novo MEIs deemed likely causative (~0.04%). finding
- ★ Coding MEIs are under strong purifying selection equivalent to that of nonsense/essential splice-site (protein-truncating) SNVs. finding
- ★ MELT can reliably ascertain MEIs from whole-exome sequencing data, validated against matched WGS. method
- ★ A bespoke tool detects processed pseudogenes genome-wide from WES via cross-hybridization of donor-gene exome baits. method
- ★ PPG donor genes are significantly enriched for loss-of-function intolerant genes (high pLI) and highly expressed genes, consistent with prior retroduplication patterns. finding
- Intronic MEIs show no detectable selective constraint, behaving similarly to synonymous SNVs. finding
- ★ A comprehensive catalogue of RT-derived variation (1129 MEIs, 576 polymorphic PPGs) across 9738 DDD trios serves as a resource and framework for evolutionary and disease studies. resource
- Most de novo RT events phaseable in this study are of paternal origin, consistent with other de novo variant classes. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Whole-exome sequencing (trio) with MEI calling via MELT | 9738 DDD trios (n=28,132 individuals), human | none | Alu/L1/SVA mobile element insertions in/adjacent to WES bait regions | MELT (mobile element locator tool) |
| Processed pseudogene detection from WES | DDD cohort, human exomes | none | presence/absence and allele counts of PPGs via discordant read pairs and split reads | bespoke PPG detection tool |
| Matched whole-genome sequencing concordance comparison | 90 overlapping DDD individuals, human | none | MEI genotype concordance between WES and WGS | — |
| Long-PCR coupled to long-read sequencing (phasing) | DDD parent-proband samples, human gDNA | none | phasing of false-negative Alu variants to nearby SNVs / mosaicism assessment | — |
| PCR validation (and capillary sequencing) | proband/paternal/maternal gDNA, human | none | de novo status confirmation of MEIs and PPGs | — |
| Gene functional annotation and expression analysis | PPG donor genes across 30 GTEx tissue types, human | none | functional enrichment (DAVID) and tissue expression of donor genes | GTEx / DAVID |
- – Identified 9 de novo MEIs (7 Alu, 2 L1) and 2 de novo gene retroduplications across 9738 trios 11 total de novo RT events
- – 4 de novo MEIs disrupted coding sequence of known DD genes (SETD5, NSD1, MEF2C, ARID2) and were likely causative 0.04% of probands
- – Exonic MEIs show selective constraint indistinguishable from nonsense and essential splice-site SNVs
- – WES MEI genotype concordance with matched WGS 94.46% overall (93.93% Alu, 97.29% L1, 98.25% SVA)
- – Re-identified MEI genotypes detectable in WGS within WES bait regions 1450 genotypes, 84.5%
- ▲ PPG donor genes enriched for high-pLI (LoF-intolerant) genes vs all protein-coding genes 25.3% vs 17.6%
- – Rate of de novo MEI events approximately one per 1000 patient exomes ~1/1000
- – Each human genome harbors ~one MEI directly impacting protein-coding sequence; MEIs make up 0.6% of coding PTVs 0.76 ± 0.62 SD per individual
- other 94.46% (93.93% Alu, 97.29% L1, 98.25% SVA) (WES vs WGS MEI genotype concordance, ≥10× coverage)
- count 1129 MEIs and 576 polymorphic PPGs; 33.0 ± 5.0 SD variants per exome (total RT variants discovered in DDD trios)
- pvalue Fisher's p = 2.6 × 10–4 (PPG donor genes enriched for high-pLI (>0.9) genes (25.3% vs 17.6%))
- correlation r2 = 0.64 (PPG relative allele counts vs Zhang et al. 1KGP assessment)
- other r2 = 0.64 / DAVID enrichment score 8.82 (ribosomal/translational gene functional cluster among PPG donor genes)
- pvalue χ2 p = 0.083 (paternal-origin bias of de novo RT events (3 phased, all paternal))
- count 84.5% (1450 genotypes) (proportion of WGS-identifiable MEI genotypes re-identified in WES bait regions)
- other 1 incorrect candidate de novo per 295 patients (1 false-positive per 649; 1 false-negative per 541) (false finding rate of the RT assessment pipeline)
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.
This is a population/statistical genomics study that systematically ascertains retrotransposition (RT)-derived variants (mobile element insertions and processed pseudogenes) from 9738 whole-exome-sequenced parent–child trios (28,132 individuals) in the DDD cohort. The analysis is largely descriptive and enumerative—reporting concordance/validation rates, per-individual variant counts summarized as mean ± SD, allele-frequency and Poisson-distribution fits—and uses categorical/count-based tests (chi-square, Fisher's exact, Wilcoxon rank-sum) plus Poisson-based simulation to compare observed versus expected de novo counts and to assess selective constraint. Results are reported with point estimates, exact or threshold p-values, and 95% confidence intervals on key proportions.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Wilcoxon rank-sum (Mann–Whitney) test | comparison of PPG donor-gene expression vs non-retroposed genes across GTEx tissues, and testis/ovary vs other tissues | 30 GTEx tissue types (28 compared for ovary/testis) | not stated |
| Fisher's exact test | enrichment of high-pLI (>0.9) genes among PPG donor genes (25.3%) vs all protein-coding genes (17.6%) | — | not stated |
| Chi-square (χ²) test | intronic MEI sense/antisense orientation bias; paternal vs maternal parental origin of de novo events | 3 of 11 de novo events phased (parental origin) | not stated |
| Poisson-distribution test based on simulation | observed vs expected de novo MEI counts in exons and enhancers for all/high-pLI/MA-DDG2P gene sets (Fig. 4) | 100 simulations using neutral mutation rate (1.2 × 10⁻¹¹ μ) | stated (Poisson model assumed; counts approximate Poisson) |
| Correlation (r²) between data sets | PPG allele counts in this study vs Zhang et al. 1KGP assessment (r²=0.64); PPG vs WGS validation | — | na |
| Genotype concordance / proportion estimation | WES vs matched WGS MEI calls (94.46% overall concordance); singleton and pLI proportions for constraint (Fig. 2b) | 90 overlapping WGS individuals; 17,032 unaffected parents for constraint | na |
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Per-individual variant counts were summarized as mean ± SD.↳ Could also: The same spread could also be conveyed with a 95% confidence interval or, given the count/Poisson nature of the data, an interquartile range or the Poisson rate with its interval. — A CI communicates precision of the mean estimate, and an IQR/Poisson interval can be a natural complement for skewed count data; all are standard ways to convey dispersion.
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Multiple per-tissue Wilcoxon rank-sum comparisons (across ~30 GTEx tissues) were evaluated against a fixed p<1×10⁻³ threshold.↳ Could also: A formal multiplicity adjustment such as Benjamini–Hochberg FDR or Bonferroni across the tissue family could also be applied alongside the threshold. — An explicit correction makes the family-wise or false-discovery control transparent when many tissues are tested, complementing the stringent fixed cutoff already used.
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Observed vs expected de novo counts were assessed using a Poisson model based on a neutral mutation rate and 100 simulations.↳ Could also: An exact Poisson test, a negative binomial model (to allow overdispersion), or a larger number of simulations could also be used. — A negative binomial or exact approach can accommodate possible overdispersion, and more simulation replicates can tighten the empirical null; these can add robustness for rare-count inference.
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Categorical comparisons (orientation bias, parental origin) were evaluated with chi-square tests, some on small counts (e.g. 3 of 11 events phased).↳ Could also: Fisher's exact test (or an exact binomial test) could also be used for these small-count comparisons. — Exact tests do not rely on large-sample chi-square approximations and are often preferred when expected cell counts are small.
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Agreement between data sets (e.g. PPG allele counts vs 1KGP) was summarized with an r² value.↳ Could also: A concordance correlation coefficient, Spearman's rank correlation, or a Bland–Altman-style agreement plot could also be reported. — Rank-based or agreement-specific metrics can characterize monotonic association and systematic offsets, complementing r² for cross-platform comparisons.
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Constraint was assessed via two summary proportions (singletons and fraction in high-pLI genes) with 95% CIs.↳ Could also: A logistic regression or other model adjusting for covariates (e.g. variant length, coverage) could also estimate constraint effects. — A modeling approach can jointly account for potential confounders and yield adjusted effect estimates, complementing the descriptive proportion comparison.
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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Processed pseudogene donor genes are enriched for LoF-intolerant (high-pLI) genes relative to all protein-coding genes (25.3% vs 17.6%)other human up 2019×1papers★ This paper is the founder (earliest)
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Each human genome harbors on average 0.76 MEIs directly impacting protein-coding sequence, comprising 0.6% of all coding protein-truncating variantsWES human 2019×1papers★ This paper is the founder (earliest)
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De novo MEIs arise at a rate of approximately 1 per 1000 developmental disorder patient exomesWES human 2019×1papers★ This paper is the founder (earliest)
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MEI genotypes called from WES show 94.46% overall concordance with matched WGS (93.93% Alu, 97.29% L1, 98.25% SVA)WES human 2019×1papers★ This paper is the founder (earliest)
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Exonic MEIs are under selective constraint indistinguishable from nonsense and essential splice-site SNVs, indicating strong purifying selection against protein-coding insertionsWES human down 2019×1papers★ This paper is the founder (earliest)
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De novo MEIs disrupt coding sequences of known DD genes (SETD5, NSD1, MEF2C, ARID2) and are likely causative in approximately 0.04% of developmental disorder probandsWES human 2019×1papers★ This paper is the founder (earliest)
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84.5% of WES-callable MEI sites within bait regions can be re-identified in matched WGS dataWGS human 2019×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
scope.md — pmid-31604926
Paper: Gardner EJ et al. "Contribution of retrotransposition to developmental disorders." Nat Commun 10, 4630 (2019). PMCID PMC6789007. DOI 10.1038/s41467-019-12520-y.
Correction to auto-enriched metadata (IMPORTANT)
The scaffold's enrichment auto-filled the WRONG artifacts:
code_url = github.com/PacificBiosciences/unanimity→ FALSE POSITIVE (PacBio CCS consensus tool, unrelated). The real code is the authors' own repos (below).data_accession = GSE74432 (GEO)→ FALSE POSITIVE as the primary dataset. GSE74432 is only a methylation reference used as a side input. The paper's primary data are DDD exomes in EGA (controlled access).
Real artifacts (from the paper's Code/Data availability)
- Code (authors' own):
github.com/eugenegardner/MEIPaper@ commit0e709a30a0f8c8667600f2af7495f14f435524f9— R-markdown notebooks (Figure1/2/4 + Supplement) that regenerate all figures & statistics derived in R, shipping their own RawData/ (small text files).github.com/eugenegardner/Retrogene@ commitd65b17039390c8bd73dc7fd7b325c846618f5c06— Java pseudogene/PPG discovery pipeline (operates on BAMs).
- Primary data (raw): EGA controlled access — study
EGAS00001000775, datasetsEGAD00001004390(WES),EGAD00001004586(RT calls + WGS). dbGaP/EGA controlled → NOT publicly obtainable.
In scope (reproducible from shipped code + shipped data) — ATTEMPTED
The MEIPaper repo ships the post-calling R analysis with the exact small input tables it consumes. This is the "pipeline-derived" downstream analysis. Pipeline = R (data.table/ggplot2/cowplot) over the shipped MELT/Retrogene output summaries.
| Result | Where in paper | Rmd / data |
|---|---|---|
| Mean ascertained MEIs per individual exome (Alu/L1/SVA/PPG/total) | Table 1 | Figure1.Rmd ← *.totals.di.txt |
| Germline MEI mutation rate (DDD μ & 1KGP, ~1.2–1.4×10⁻¹¹ /bp/gen) | Table 2 / Results | Figure4.Rmd ← hardcoded site totals |
| Genome-wide rate: ~1 new MEI per 12–14 births | Results | Figure4.Rmd |
| Expected # de novo MEIs in 9738 individuals | Results | Figure4.Rmd |
| "Coin-flip" P that 4/6 exonic de novos hit MA-DDG2P (~10⁻⁵) | Results/Methods | Figure4.Rmd |
| de novo enrichment Poisson P-values (Fig 4) | Fig 4 | Figure4.Rmd ← de_novo.test.txt |
| 1KGP downsampling significance (Alu 318 / L1 81 / SVA 26 vs DDD) | Supp Fig 4 | Supplement.Rmd ← *_random.txt |
| Mosaic/VAF Wilcoxon tests (parents vs probands vs hets) | Supp Fig 2 | Supplement.Rmd ← genodepth.di.txt |
| PPG vs Zhang/Gerstein allele-count regression r² | Supp Fig 5 | Supplement.Rmd ← gerstein.txt, Retrogenes.di.txt |
| PPG tissue-expression / pLI Fisher test | Supp Fig 6 | Supplement.Rmd ← GTEx.expr.txt, ExacPLI.txt |
| Intronic MEI strand-bias chi-square (Alu/L1/SVA) | Supp Fig 8 | Supplement.Rmd ← strands.txt |
| MEI allele-frequency spectrum & length distributions | Fig 1F/1G | Figure1.Rmd ← *.frq, *.len |
| Exon/intron pLI enrichment (Fig 2) | Fig 2 | Figure2.Rmd |
Out of scope (cannot reproduce from public artifacts) — NOT attempted, with reason
| Step | Reason |
|---|---|
| MELT MEI calling from raw exomes | Input = EGA controlled-access DDD WES (EGAD00001004390). Not obtainable. |
| Retrogene/PPG calling from raw BAMs | Same restricted BAMs; Java tool present but no input data. |
| Wet-lab validation (PCR, capillary, Figure 3 blots) | Manual/experimental; not a pipeline. |
| Clinical pathogenicity calls ("4 likely causative") | Manual clinician review; not computational. |
Reproduction strategy
Raw-data re-calling is blocked by controlled access (a legitimate partial blocker), but the downstream statistical pipeline is fully shipped (code + its input summary tables). We rerun the R notebooks on «our HPC» and compare the regenerated numbers 1:1 against Table 1, Table 2, and the Results/Supplement text. This is an "own-code on shipped data" reproduction — honest partial: downstream analy
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.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
On everything testable this is a faithful 1:1 reproduction: all six Table 1 per-individual means±SD and both Table 2 germline rates (1.21e-11 / 1.40e-11) match to printed precision, the 12–14 births/MEI range and coin-flip P (~1e-5) reproduce, with deviations only at the rounding/stochastic level and no fabrication signal. The only gaps sit on the data-availability side, not the authors' side: primary exomes/BAMs are EGA controlled-access, so MELT/Retrogene calling, the 9 de-novo count (C16), and the manually-curated numerator behind the 1/2434 diagnostic fraction (C17) could not be independently re-derived. Net: exact where reproducible, honestly partial where gated by restricted data — overall solid but not a clean full-coverage green.
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.