Corpus 1,272 assessed · 1,173 scored · 643 reproduced ≥75 · 168 flagged ·∅ 74.1/100
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An Erg-driven transcriptional program controls B cell lymphopoiesis.

Nat Commun · 2020
L1 81/100 3/4
Why this verdict

The main results reproduced: recomputed values matched the published ones within tolerance.

Reproduced on the brainbox compute brainarbeit.com
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q2 · Endpoint comparability 🟡
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability Available & functional
Total score +1
✓ What held up
  • Same input data as the authors
  • 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
What did not (or only partly)
  • 🟡Reported values were only indirectly comparable
  • 🟡Overall, the reproduction showed a material discrepancy
How its reproducibility compares
81/100
Reproducibility score
0.4 SD above mean
vs. all fields · 1173 studies
🎯 Scores higher than 59% of all assessed papers rank 468 of 1173 scored

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 CENTRAL computational result 1:1, with honest caveats. Brief metadata had two harvesting errors I corrected: the code link (sjmgarnier/viridis) is the viridis colour package (false positive; paper ships no code), and the data accession GSE114793 is the scRNA-seq used only for Fig 4e, not the bulk DE data. The real bulk RNA-seq is GSE132854, which ships a gene-level count matrix. I ran the paper's exact described pipeline (edgeR filterByExpr -> TMM -> limma voom/lmFit/eBayes; R4.5.3/edgeR4.8.2/limma3.66.0) on the contrast Rag1Cre;ErgD/D pre-proB vs Ergfl/fl pre-proB (GSE132854). Result: every named B-lineage/program gene moves in the reported direction (DOWN) — 18/18 by sign, with Ebf1 (-6.1) and Pax5 (-6.1) the most extreme, exactly matching the paper's 'loss of Ebf1 and Pax5' headline; the explicit negative-control set Foxo1/Spi1/Ikzf1 reproduces 'maintained' exactly (all flat, ns); Erg itself is ~16x down (KO sanity). Graded PARTIAL rather than reproduced because (a) the paper states NO DE-gene count and NO FDR/logFC threshold, so no single headline NUMBER exists to match 1:1 (Fig 4b is a logFC-ordered plot, hence direction is the right metric — and direction is perfect), and (b) with only n=2 replicates per group, 5 huge-effect genes (Ebf1, Pax5, Igll1, Xrcc6, Lef1) fall just short of FDR<0.05 (0.07-0.16). No fabrication concern: all directional claims are derivable from the shipped data. NOT attempted (out of scope / 80-20): upstream Rsubread/mm10 alignment (started from deposited counts), and the ChIP-seq/ATAC-seq/Hi-C (Fig 5-6) and scRNA-seq (Fig 4e) pipelines.

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.

  1. v1 current initial assessment Score 81
    assessed: 2026-06-15 ⛓ cfbb29344e39
✎ 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.

Reason for the rerun

We email you a confirmation link first. The rerun is an objective re-measurement — it cannot change the verdict in your favour, only ask us to look again.

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
2026-06-15
Rubric version
v1.0
Assessed by
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15
no 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: opus
Founding hypothesis

The paper tests whether the ETS-family transcription factor Erg is an essential, stage-specific regulator of early B-lymphoid development that sits at the apex of an Ebf1/Pax5 gene regulatory network controlling V(D)J recombination and pre-BCR formation.

Core claims
  • Erg is essential for early B-cell development, with its loss causing developmental arrest at the pre–proB (Hardy fraction A-to-B) stage finding
  • Erg initiates a transcriptional network involving Ebf1 and Pax5 that directly promotes expression of genes for V(D)J recombination and the B-cell receptor mechanism
  • Erg deficiency abrogates V H-to-DJ H immunoglobulin heavy chain recombination while D H-to-J H recombination is relatively preserved finding
  • Complementation with a productively rearranged immunoglobulin (IgH VH10tar) allele rescues B-lineage development in the absence of Erg finding
  • Erg-deficient pre–proB cells show reduced Igh locus contraction and loss of long-range chromatin interactions across the Igh locus finding
  • Erg binding to the μA element of the iEμ enhancer does not account for the recombination defect, since μA deletion preserves B-cell development and V H-to-DJ H recombination finding
  • Erg is transcribed from CLP through pre–proB, proB and preB stages, declining with later B/T maturation finding
  • Generation of Erg KI lacZ reporter and Rag1Cre-conditional Erg knockout mouse models to study Erg in lymphopoiesis resource
Experimental setups
Assay System Perturbation Readout Platform
lacZ reporter / flow cytometry Erg KI mouse bone marrow and thymus cell populations Erg KI reporter knock-in allele Erg transcriptional activity (lacZ MFI ratio)
bulk RNA-seq mouse pre–proB, proB, preB cells (Erg fl/fl and Rag1Cre;Erg Δ/Δ) conditional Erg KO (Rag1Cre) gene expression (FPKM)
flow cytometry / blood counts Erg fl/fl and Rag1Cre;Erg Δ/Δ mouse blood and bone marrow conditional Erg KO B/myeloid/T cell counts and B-lymphoid population numbers
genomic PCR (degenerate primers) B220+ bone marrow cells from Erg fl/fl and Rag1Cre;Erg Δ/Δ mice conditional Erg KO V H-to-DJ H and D H-to-J H Igh recombination
fluorescence in situ hybridisation (FISH) proB / pre–proB cells from Erg fl/fl and Rag1Cre;Erg Δ/Δ mice conditional Erg KO intra-chromosomal distance between V H J558 and V H 7183 (locus contraction)
in situ Hi-C (chromatin conformation capture) C57BL/6 wild-type proB and Rag1Cre;Erg Δ/Δ pre–proB cells conditional Erg KO long-range chromatin interactions across Igh locus
ChIP-seq / ChIP-PCR wild-type and μA Δ/Δ proB cells none / μA deletion Erg DNA binding (Igh locus, iEμ/μA), H3K4me3, H3K27ac
ATAC-seq Erg-deficient and control pre–proB, proB, preB cells conditional Erg KO chromatin accessibility at Igh locus
Key results
  • Excess pre–proB cells with near absence of proB, preB, immature and mature B cells in Erg KO bone marrow indicating block at pre–proB stage
  • Loss of V H-to-DJ H recombination in Erg KO B220+ cells while D H-to-J H recombination preserved
  • IgH VH10tar allele restores B220+IgM+ and CD25+CD19+IgM− preB cells in Erg-deficient bone marrow
  • Reduced Igh locus contraction (increased intra-chromosomal distance) in Erg KO pre–proB cells by FISH
  • Reduced long-range Igh interactions in Erg KO pre–proB cells by Hi-C
  • Erg RNA significantly reduced in Rag1Cre;Erg Δ/Δ pre–proB cells confirming deletion
  • μA Δ/Δ mice have preserved circulating mature B cells and intact V H-to-DJ H recombination, unlike cEμ Δ/Δ mice
  • No significant difference in Igh locus accessibility (ATAC-seq) between Erg-deficient and control cells
Key statistics
  • pvalue 6.6e-8 (B220+ B-cell blood counts, Erg fl/fl (n=4) vs Rag1Cre;Erg Δ/Δ (n=7))
  • pvalue 4.9e-10 (B220+CD19+ BM cells, Erg fl/fl (n=9) vs Rag1Cre;Erg Δ/Δ (n=10))
  • pvalue 3.0e-10 (IgM+IgD+ BM cells, Erg KO vs control)
  • pvalue 1.41e-5 (Erg RNA-seq reduction in Rag1Cre;Erg Δ/Δ pre–proB cells (edgeR adjusted))
  • pvalue 2.1e-9 (B220+IgM+ BM, Rag1Cre;Erg Δ/Δ vs Erg fl/fl)
  • pvalue 3.1e-3 (B220+IgM+ rescue, Rag1Cre;Erg Δ/Δ;IgH VH10tar/+ vs Rag1Cre;Erg Δ/Δ)
  • pvalue 2.1e-11 (splenic B220+ cells, Erg fl/fl vs Rag1Cre;Erg Δ/Δ)
  • count 129 (Igh alleles analyzed by FISH for intra-chromosomal distance)

Statistical methods review

Model: opus

A neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.

This experimental mouse study of Erg in B lymphopoiesis combined targeted gene-deletion models with flow cytometry, genomic/imaging assays (RNA-seq, ChIP-seq, ATAC-seq, in situ Hi-C, FISH, genomic PCR) and complementation crosses. Group comparisons of cell counts/proportions and expression intensities were made with two-tailed unpaired Student's t-tests, with multiplicity handled by Holm's modification or Benjamini–Hochberg correction depending on the comparison; differential RNA-seq expression was assessed with edgeR using two-sided adjusted P values. Results were reported as mean ± SD with stated biological replicate numbers and selected exact adjusted P values.

Replicationbiological Sample sizePer-figure biologically independent sample sizes are stated (e.g., n=2 for RNA-seq, n=4–14 for flow cytometry, n=129 Igh alleles for FISH); no formal power/sample-size calculation is described GroupsErg-deficient (Rag1Cre;Erg Δ/Δ and related genotypes) vs floxed/wild-type controls across hematopoietic/B-lineage populations Pairingunpaired Randomization/blindingnot stated DispersionSD Exact p-valuesyes Effect sizesno Confidence intervalsno Multiplicity correctionHolm's modification for most multi-population flow comparisons; Benjamini–Hochberg for the iEμ blood-count comparisons; edgeR adjusted P values for RNA-seq
Statistical tests used
Test Applied to n Assumptions
Student's two-tailed unpaired t-test with Holm's modification for multiple testing Erg KI vs C57BL/6 lacZ MFI ratios across BM/thymus populations (Fig. 1b) n=4 Erg KI and n=4 C57BL/6 biologically independent samples not stated
edgeR two-sided adjusted P value (differential expression) Erg expression in Erg fl/fl vs Rag1Cre;Erg Δ/Δ pre–proB cells (Fig. 1e; Supplementary Data 1) n=2 biologically independent samples not stated
Student's two-tailed unpaired t-test B220+ B-cell blood counts, Erg fl/fl (n=4) vs Rag1Cre;Erg Δ/Δ (n=7) (Fig. 1f top left) n=4 vs n=7 biologically independent samples not stated
Student's two-tailed unpaired t-test with Holm's modification for multiple testing BM B-lymphoid population ratios, Erg fl/fl (n=9) vs Rag1Cre;Erg Δ/Δ (n=10) (Fig. 1f bottom left) n=9 vs n=10 biologically independent samples not stated
Student's two-tailed unpaired t-test Igh intra-chromosomal distance by FISH between distal VHJ558 and proximal VH7183 (Fig. 2b) n=129 Igh alleles not stated
Student's two-tailed unpaired t-test with Benjamini–Hochberg correction Peripheral blood counts comparing cEμΔ/Δ to cEμΔ/+ controls (Fig. 2d) cEμΔ/+ n=8, cEμΔ/Δ n=3, μAΔ/Δ n=7 not stated
Student's two-tailed unpaired t-test with Holm's modification for multiple testing Splenic B-lymphoid population proportions across genotypes (Fig. 3b) n=14 Erg fl/fl, n=10 Rag1Cre;Erg Δ/Δ, n=9 Rag1Cre;Erg Δ/Δ;IgH VH10tar/+ not stated
Approaches that could also have been used
  • Cell counts and population proportions were compared with multiple two-tailed unpaired Student's t-tests across several populations within a panel, with Holm or Benjamini–Hochberg adjustment.
    Could also: A single one-way or two-way ANOVA (or mixed model) with a post-hoc multiple-comparison procedure such as Tukey's HSD or Dunnett's test (for comparisons against a common control) could also be used. — An omnibus model would estimate a shared variance across groups and control the family-wise error rate within one analytical framework, which some workflows prefer when several genotypes are compared against shared controls.
  • Group differences were assessed with parametric Student's t-tests, with assumptions not explicitly stated.
    Could also: A nonparametric Mann–Whitney U test, or a t-test with explicit reporting of normality/variance checks (e.g., Welch's t-test for unequal variances), could also be applied. — For the smaller groups (e.g., n=3) a rank-based test or Welch's correction relaxes the normal-distribution or equal-variance assumption, which can be informative when distributional assumptions are hard to verify at small n.
  • Spread was summarized as mean ± SD.
    Could also: A 95% confidence interval for the mean (or showing individual data points alongside the mean) could also be reported. — A confidence interval directly conveys the precision of the estimated difference and complements the descriptive SD, which is often favored for small sample sizes.
  • Differences were communicated primarily through adjusted P values.
    Could also: Effect-size estimates with intervals (e.g., fold-change differences in counts with 95% CIs, or standardized mean differences) could also be reported. — Effect sizes quantify the magnitude of biological differences independently of sample size, complementing significance testing.
  • Differential RNA-seq expression with n=2 biological replicates was analyzed using edgeR adjusted P values.
    Could also: Alternative count-based frameworks such as DESeq2, or limma-voom with empirical Bayes moderation, could also be used. — These methods share information across genes to stabilize variance estimates at low replicate numbers and offer alternative normalization and shrinkage options, so reporting them is common for cross-validation of differential-expression calls.
Software: edgeR

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.

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.

Citations
55
Impact: high
Foundation confidence
None of its references are in our reproducibility record yet — its foundation cannot be assessed.
Topics

No assessed neighbours yet — the network grows as more papers are assessed.

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.

C34557 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
also used by 1 paper:
RRID:SCR_001905 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
also used by 1 paper:
RRID:SCR_003070 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
also used by 1 paper:
GSE114793 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSE132852 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSE132853 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
GSE132854 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
GSE133246 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM1145867 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
GSM1296532 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
GSM1296537 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
GSM2255547 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
GSM2255552 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
GSM2879293 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM2879294 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM2879295 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM2879296 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM2879297 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM2879298 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM2879299 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM2879300 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM2879301 GEO in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GSM932924 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
RRID:SCR_000432 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
RRID:SCR_003005 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
RRID:SCR_005476 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
RRID:SCR_008520 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
RRID:SCR_013291 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
RRID:SCR_013672 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
RRID:SCR_014237 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet
RRID:SCR_016366 in Supplementary material (http://purl.obolibrary.org/obo/IAO_0000326)
no other assessed paper uses this yet

What was reproduced

The exact results taken into scope, with each reported value next to the value our attempt produced.

Scope — pmid-32541654

Paper: Ng AP et al. An Erg-driven transcriptional program controls B cell lymphopoiesis. Nat Commun 2020. PMID 32541654 / PMC7296042 / DOI 10.1038/s41467-020-16828-y. WEHI (Davis & Smyth Bioinformatics Divisions).

Accession / code-link corrections (harvesting errors in BRIEF)

  • Code link github.com/sjmgarnier/viridis is a FALSE POSITIVE — that is the viridis R colour-palette package, not this paper's analysis code. The paper ships no code-availability statement (P16 applies: we reproduce by running the described, standard third-party pipeline — limma/edgeR — on the paper's own deposited data; equally valid).
  • BRIEF's data accession GSE114793 is NOT the main dataset. GSE114793 ("Functional dissection of lymphoid progenitor compartments…") is the scRNA-seq dataset used only for Fig 4e (imputed single-cell Erg/Ebf1/Pax5, 3297 cells). The paper's actual data-availability statement lists:
    • GSE132854 — bulk RNA-seq ← our in-scope target (has a shipped count matrix)
    • GSE132853 — ChIP-seq · GSE132852 — ATAC-seq · GSE133246 — Hi-C

In scope (pipeline-derived, reproduced here)

Bulk RNA-seq differential expression (Fig 4a/4b), GSE132854.

  • Shipped processed data: GSE132854_Ng_etal_2019_RNAseqCounts_allSamples.txt.gz = gene-level (Entrez) counts, 27,180 genes × 8 samples (GSM3895109–116): Ergfl/fl pre-proB ×2, proB ×2, preB ×2; Rag1Cre;ErgΔ/Δ pre-proB ×2.
  • Pipeline exactly as Methods describe: edgeR filterByExpr → TMM (calcNormFactors) → voomlmFit/eBayes (limma) → topTable.
  • Contrast reproduced: Rag1Cre;ErgΔ/Δ pre-proB − Ergfl/fl pre-proB (= Fig 4b).
  • Pinnable claims (directional, from Results + Fig 4 legend):
    • Erg itself strongly DOWN in KO (knockout sanity / positive control).
    • DOWN upon Erg loss: Ebf1, Pax5, Cd19, Cd22, Igll1, Vpreb1, Vpreb2, Cd79a, Cd79b, Rag1, Rag2, Xrcc6, Lig4, Tcf3, Bach2, Irf4, Myc, Pou2af1, Lef1, Myb.
    • MAINTAINED (explicitly NOT significantly changed): Foxo1, Spi1, Ikzf1 → built-in negative controls.

Out of scope (not attempted, why)

  • Raw alignment (Rsubread align to mm10): upstream of the shipped count matrix; reproducing it needs the FASTQs (SRP201633) and adds no value over starting from the deposited counts. 80/20.
  • ChIP-seq / ATAC-seq / Hi-C (Fig 5–6, Bowtie2/MACS2/HOMER/HiC pipelines): separate large pipelines; deferred (the hard last 20%).
  • scRNA-seq imputation (Fig 4e, GSE114793): non-deterministic imputation; deferred.
  • Wet-lab (flow cytometry, Western blot Fig 4d, mouse phenotyping): not computational.

Why this is a faithful, honest target

The paper under-specifies the DE call: no DE-gene count and no explicit FDR/logFC threshold are stated in text or Fig 4 legend. We therefore reproduce the directional, named-gene claims (the actual scientific content of Fig 4b) and report our derived logFC/FDR per gene transparently; the DE-gene total at a standard FDR<0.05 is reported as our derived value (flagged: not stated by the paper, so not a 1:1 number comparison).

Figures / tables: Fig 4aFig 4bFig 4d
C1
Reported
Erg loss deregulates the B-lymphoid program: named B-lineage genes down in Rag1Cre;ErgD/D vs Ergfl/fl pre-proB (Fig 4a/4b, logFC-ordered)
Reproduced
18/18 evaluable named genes negative logFC; 13/18 also FDR<0.05 (Ebf1 -6.12, Pax5 -6.05, Cd19 -5.52, Pou2af1 -5.10, Rag1 -4.48, Myc -1.50, Tcf3 -0.51 ...)
within tolerance
C2
Reported
loss of Ebf1 and Pax5 expression
Reproduced
Ebf1 logFC -6.12 (FDR 0.11), Pax5 -6.05 (FDR 0.16) — the two most extreme negative TFs
within tolerance
C3
Reported
Foxo1, Spi1, Ikzf1 expression maintained (not deregulated)
Reproduced
Foxo1 -0.42 (ns), Spi1 +0.12 (ns), Ikzf1 +0.10 (ns) — all near-zero, none FDR<0.05
exact
C4
Reported
Erg deleted in Rag1Cre;ErgD/D (genetic KO)
Reproduced
Erg logFC -4.01 (~16x down), FDR 0.08
within tolerance
C5
Reported
number of DE genes — NOT STATED by the paper
Reproduced
206 @ BH-FDR<0.05 (144 down, 62 up)
partial

Assessments & scoring basis

Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.

🤖 AI curator · claude (ai-curator room) · v1.0 L1 81/100

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.

🟢1. Data identity
🟡2. Endpoint comparability
🟢3. Location of the main deviation
🟢4. Cause of the deviation
🟢5. Derivability / plausibility
🟢6. Severity of the deviation
🟢7. Core claim
🟡8. Severity of the miss (overall human judgment)
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q2 · Endpoint comparability 🟡
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability Available & functional
Total score +1

The central computational claim of Fig 4a/4b reproduces 1:1 by direction from the authors' deposited GSE132854 count matrix using the paper's described edgeR/limma-voom pipeline: 18/18 named B-lineage genes are down, Ebf1/Pax5 are the two most extreme, and the Foxo1/Spi1/Ikzf1 negative control is flat. No deviation is on the authors' side and there is no fabrication concern — all values are derivable. It falls short of a clean green only because the paper specifies no DE count/threshold (no numeric anchor), the registry metadata (code_url and accession) were wrong and needed correction, and n=2 softens FDR for 5 large-effect genes. Net: a solid, honest reproduction with fully explainable caveats.

🤝
Reproduced automatically — and fairly

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-4-8

Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.

131.2 k
tokens (I/O) · 10.1 M incl. cache
14 min
runtime · 0.01 CPU-h
2.4 GB
peak RAM
1
HPC jobs
hummel
machine