A cross-species approach to identify transcriptional regulators exemplified for Dnajc22 and Hnf4a.
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
- ✓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
- ✓Overall, the reproduction was clean
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
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 (described well enough; honest 1:1 on the authors' own code + data). The paper's computational core is co-expression of a gene-of-interest with candidate TFs across tissues; its headline computational claim is that Hnf4a is strongly co-expressed with (a top correlate of) Dnajc22 across species. Re-running the authors' own two R pipelines (repo schultzelab/Dnajc22-regulation @ 72f3d14) on the paper's own data on «our HPC» reproduces this 1:1 in BOTH species: mouse Affymetrix GSE10246 (103 CEL -> RMA -> tissue-mean -> cor) gives r(Dnajc22,Hnf4a)=0.95 with Hnf4a the #1 correlate of all 18,197 genes; human RNA-seq GSE16256/Roadmap (DESeq2 -> tissue-mean -> cor, counts shipped in the repo) gives r(DNAJC22,HNF4A)=0.99 with HNF4A the #1 correlate of all 16,140 genes. The paper says 'one of the highest correlations' / 'strong relationship'; our reproduction shows Hnf4a is in fact THE single highest correlate in both species (if anything the paper understates). A cross-check (DESeq2 re-normalisation from the shipped raw counts reproduces the shipped normalised table's correlation exactly) shows no fabrication in that artifact. NOT attempted (out of scope / 80-20): the SOM 225-gene cluster (shipped SOM code has placeholder bugs: som.input2 undefined, genename='Creld1', hard-coded cluster indices; seed/scale sensitive -> not faithfully runnable as shipped); pcaGoPromoter/iRegulon TF prediction (iRegulon is a Cytoscape GUI; pcaGoPromoter PRIMO depends on the unreproducible SOM membership); WGCNA/BioLayout network; all wet-lab (ChIP-qPCR, luciferase) and fly/zebrafish validation; the exact printed heatmap r cells (inside Supplementary Figure images) were not OCR-compared. «our HPC» notes: bioconda Bioconductor data/annotation packages (genomeinfodbdata, mouse4302.db/cdf, org.Mm.eg.db) have post-link downloads unreachable from compute nodes -> installed source tarballs from the tu-dortmund Bioc mirror; preprocessCore rebuilt --disable-threading to clear the affy::rma pthread_create=22 cgroup bug. These grades are PROVISIONAL; a human auditor decides ground truth.
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
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v1 current initial assessment Score 76assessed: 2026-06-16 ⛓ 1bb4f7947e44
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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
- 2026-06-16
- 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
- 2026-09-19
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: sonnetTranscription factors (TFs) and the genes they regulate are co-expressed across tissues and species, and this principle can be exploited computationally to identify the transcriptional regulator of a gene of interest; the paper tests whether this approach correctly identifies Hnf4a as the TF regulating the uncharacterized gene Dnajc22.
- ★ Hnf4a is a major transcriptional regulator of Dnajc22 finding
- ★ A combined co-expression (SOM clustering) and TF binding site prediction approach can identify candidate regulatory TFs for a gene of interest method
- ★ Hnf4a/HNF4A directly binds the Dnajc22/DNAJC22 locus in rat, mouse, and human finding
- ★ The H4 motif (+50 bp) is the functionally relevant Hnf4a binding site in the murine Dnajc22 promoter finding
- ★ Hnf4a-mediated regulation of the Dnajc22 ortholog is conserved in Drosophila, zebrafish, and human finding
- An independent network approach (WGCNA) also predicts Hnf4a as a regulator of Dnajc22, corroborating the SOM-based approach method
- Dnajc22 is the vertebrate ortholog of Drosophila wurst, previously shown essential for tracheal development resource
- ★ The described cross-species in silico plus wet-lab workflow is a generalizable strategy for identifying TF-GOI regulatory relationships method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| co-expression clustering (self-organizing map, hierarchical clustering) | murine tissue atlas transcriptomes (22 organs, 14 tissues, 16 cell types) | none | TF-GOI co-expression cluster membership | GSE10246 |
| co-expression clustering (self-organizing map) | human Roadmap Epigenomics transcriptomes (6 organs, 5 tissues, 2 cell types) | none | TF-GOI co-expression cluster membership | GSE16256 |
| transcription factor binding site prediction | in silico murine Dnajc22 promoter locus | none | predicted HNF4 binding sites/motifs | TFBIND, PROMO, MATCH, iRegulon, pcaGoPromoter |
| reanalysis of published microarray/transcriptome datasets from Hnf4a loss- and gain-of-function experiments | mouse liver-specific Hnf4a KO; human hepatocellular carcinoma HNF4A siRNA knockdown; HEK293 HNF4A overexpression; HCT116 HNF4A overexpression; rat INS-1 cells HNF4A/HNF1B/HNF6 expression; human renal carcinoma tissue | Hnf4a knockout/knockdown/overexpression | Dnajc22/DNAJC22 transcript levels | GEO microarray datasets |
| ChIP-seq reanalysis | rat kidney, mouse intestine, human HCT116 colon carcinoma cells | none | Hnf4a binding peaks at Dnajc22 locus | MACS2 peak calling |
| ChIP-qPCR | murine kidney cortex | none | Hnf4a binding enrichment at Dnajc22 promoter fragment (vs Apoc3 positive and Hprt1 negative controls) | — |
| luciferase reporter assay | murine M-1 kidney cell line | heterologous HNF4A overexpression; site-directed mutagenesis of predicted binding sites (ΔH1-H4) | luciferase activity | — |
| quantitative real-time PCR | Drosophila melanogaster Hnf4 mutant larvae; zebrafish embryos injected with human HNF4A; HEK293 cells overexpressing HNF4A | Hnf4a loss-of-function (fly); HNF4A gain-of-function (zebrafish, HEK293) | wurst/dnajc22/DNAJC22 transcript levels | — |
- – Dnajc22 shows predominant expression in liver, kidneys, and intestine
- ▼ Liver-specific Hnf4a knockout reduces Dnajc22 transcript to background levels
- – HNF4A knockdown by siRNA decreases DNAJC22 levels; HNF4A overexpression in HEK293 and HCT116 cells induces DNAJC22 expression
- – Hnf4a ChIP-seq/ChIP-qPCR shows binding peaks near the Dnajc22 transcription start site in rat, mouse, and human, confirmed by ChIP-qPCR in murine kidney cortex
- ▲ Heterologous HNF4A expression increases luciferase activity of the Dnajc22 promoter construct relative to control 10.45-fold, P = 0.02, n = 6
- ▼ Mutation of the H4 binding site abolishes HNF4A-induced luciferase activity, reducing it to background levels; mutation of H1-H3 does not P = 0.0003, n = 4
- – H4 motif shows the highest cross-species conservation among the four predicted binding sites
- – Hnf4a-Dnajc22 co-regulation is confirmed in Drosophila (wurst), zebrafish, and human HEK293 cells
- fold_change 10.45 fold (luciferase activity increase with WT Dnajc22 promoter construct after HNF4A expression)
- pvalue P = 0.02 (significance of luciferase increase for WT Dnajc22 promoter construct)
- pvalue P = 0.0003 (significance of luciferase activity reduction for ΔH4 mutant construct)
- count 225 genes (number of genes in the Dnajc22-containing SOM cluster)
- count 19 genes (genes in Dnajc22-containing subcluster after hierarchical clustering, 5 of which were TFs)
- count 104 transcriptomes (mouse gene atlas transcriptomes analyzed (of 182 total in GSE10246))
- count 25 transcriptomes (human Roadmap Epigenomics transcriptomes analyzed from GSE16256)
- other 49 species (number of species with single orthologs of wurst/Dnajc22 per Ensembl)
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 combines computational co-expression clustering (self-organizing maps, WGCNA, Pearson correlation across tissue transcriptome atlases) with experimental wet-lab validation to identify Hnf4a as a transcriptional regulator of Dnajc22. Significance testing in wet-lab experiments relied on one-sided t-test variants (unpaired, one-sample, and paired) applied separately to each dataset or assay. Results are reported with selective exact p-values and one fold-change estimate; no correction for multiple comparisons across the many independent tests is described.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| one-sided t-test (unpaired) | Re-analysis of six GEO transcriptome datasets for Hnf4a perturbation effects on Dnajc22 expression (Fig. 2a–f) | varies by dataset: n=3, 4, 2, 3, 2, 4 | not stated |
| one-sided one-sample t-test | ChIP-qPCR enrichment of Hnf4a at the Dnajc22 locus in murine kidney cortex (Fig. 3d) | — | not stated |
| one-sided paired t-test | Luciferase reporter assays in M-1 cells with heterologous HNF4A expression, wild-type and single-site mutant constructs (Fig. 4b, 4d) | n=6 (Fig. 4b); n=4 (Fig. 4d) | not stated |
| one-sided paired t-test | qPCR validation of Dnajc22 co-regulation with Hnf4a in Drosophila, zebrafish, and HEK293 cells (Fig. 5a–c) | three independent experiments per panel | not stated |
| Pearson's correlation coefficient | Co-expression matrix of Dnajc22-related family members and predicted TFs across murine tissues (Supplementary Fig. 2b) | 104 murine transcriptomes | not stated |
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All inferential tests throughout the wet-lab validation were one-sided (one-sided t-test, one-sided one-sample t-test, one-sided paired t-test)↳ Could also: Two-sided versions of the same t-tests could also have been applied — Two-sided tests do not require a prior directional hypothesis to be specified; they are the conventional default in many reporting guidelines and remain valid even when the direction of an effect is anticipated, while guarding against undocumented directional assumptions
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Multiple separate significance tests were performed across six independent GEO re-analysis comparisons and three cross-species qPCR experiments with no stated multiplicity adjustment↳ Could also: A Benjamini-Hochberg FDR correction or Bonferroni adjustment could also have been applied across this family of tests — Adjusting for the number of comparisons controls the expected rate of false discoveries; this is commonly described when many tests are performed within a single study, and its absence or explicit justification is often requested by reviewers
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Some wet-lab groups had very small sample sizes (n=2), and parametric t-tests were used throughout↳ Could also: Non-parametric alternatives such as the Wilcoxon signed-rank test (for paired designs) or exact permutation tests could also have been applied — At n=2 the normality assumption underlying t-tests cannot be assessed empirically; non-parametric and permutation-based approaches make no distributional assumptions, though they also have very limited power at such small n
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Pearson's correlation coefficients were used to build the co-expression matrix across tissues↳ Could also: Spearman's rank correlation could also have been used — Spearman correlation is robust to outliers and to monotone non-linear relationships and is widely used for microarray and RNA-seq data, which can be right-skewed or contain extreme values that disproportionately influence Pearson coefficients
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No dispersion measure (SD, SEM, or CI) is reported or depicted for any of the wet-lab quantitative results↳ Could also: Reporting SD or 95% CI alongside point estimates could also have been standard practice — Dispersion measures communicate data variability and estimation precision, which are especially informative at small n; many journals' statistical reporting checklists explicitly request them alongside means and p-values
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SOM clustering was used as the primary co-expression grouping method, with WGCNA presented only as a supplementary confirmatory analysis↳ Could also: WGCNA or hierarchical clustering with a defined linkage criterion could also have served as the primary framework, with SOM as a cross-validation — Different clustering algorithms make different assumptions about cluster topology and gene-module boundaries; presenting two methods with equivalent status and comparing their outputs can strengthen the case that identified co-expression relationships are robust to the choice of algorithm
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 — pmid-28642491
A cross-species approach to identify transcriptional regulators exemplified for Dnajc22 and Hnf4a. Aschenbrenner, Bassler, Brondolin, Bonaguro, Carrera, Klee, Ulas, Schultze, Hoch. Sci Rep 2017; 7:4365. PMCID PMC5481429. DOI 10.1038/s41598-017-04370-9.
- Code: https://github.com/LIMES-immunogenomics/Dnajc22-regulation
(redirects to schultzelab/Dnajc22-regulation, default branch
master, HEAD commit72f3d1463e7605a6ec27a843c9d1b3f9d47909c7, pushed 2017-04-11, public, not archived, no license file). Authors' OWN analysis code. - Data:
- Mouse: GEO GSE10246 (Lattin/GNF mouse gene-atlas, Affymetrix
Mouse430 2.0). Paper used 104 of 182 transcriptomes (22 organs, 14 tissues,
16 cell types). Sample→tissue map shipped in repo
(
affy_scripts/sample_info.txt,samples_*-dataset.txt). - Human: RNA-seq HTSeq count tables for 13 tissues (×1–2 replicates, 26
files) shipped inside the repo (
RNA-Seq_scripts/expression-files/ *_counted.txt) plus the authors' DESeq-normalised table (RNA-Seq_scripts/DESeq-norm_ex_tbl.txt). No external download needed.
- Mouse: GEO GSE10246 (Lattin/GNF mouse gene-atlas, Affymetrix
Mouse430 2.0). Paper used 104 of 182 transcriptomes (22 organs, 14 tissues,
16 cell types). Sample→tissue map shipped in repo
(
Nature of the paper
Methods/proof-of-concept paper: a generic in-silico workflow (co-expression by self-organizing maps + sequence-based TF-binding prediction) to nominate a transcriptional regulator, exemplified on Dnajc22 → Hnf4a, then validated with wet-lab (ChIP-qPCR, luciferase) and cross-species (fly, zebrafish, human). The computational core is two R pipelines (mouse affy + human RNA-seq) that: RMA/DESeq-normalise → background-filter → unique-gene → SOM-cluster → pcaGoPromoter PRIMO TF prediction → Pearson correlation matrix of the GOI vs candidate TFs/family members.
In scope (pipeline-derived, attempted)
The repo's two R scripts are the pipeline. The cleanest pinnable, low-hanging
quantitative output is the Pearson correlation between Dnajc22/DNAJC22 and
Hnf4a/HNF4A across tissues — the paper's central quantitative co-expression
claim ("One of the highest correlations was observed between Hnf4a and Dnajc22",
Results; Suppl. Fig. 2b mouse / Suppl. Fig. 4 human). cor() of R is named
explicitly in Methods ("Pearson correlation coefficient matrix").
- DP-human (primary, self-contained — data shipped in repo):
run
RNASeq_pipeline.Rlogic headless on the shipped HTSeq counts (DESeq2 size-factor normalise → floor → ≥200 background filter → tissue-mean → log10 →cor). Report r(DNAJC22, HNF4A) across human tissues and HNF4A's rank among DNAJC22's correlations. Cross-check against shippedDESeq-norm_ex_tbl.txt. Maps to Suppl. Fig. 4. - DP-mouse (primary): download GSE10246 CEL for the 104 paper samples on a
«our HPC» compute node →
affyRMA → background filter (>6) → unique-gene → tissue-mean →cor. Report r(Dnajc22, Hnf4a) and Hnf4a's rank among Dnajc22's correlations within the 16-gene TF/family panel of the script. Maps to Fig. 1 / Suppl. Fig. 2b. - DP-tf (secondary, attempt-if-time): pcaGoPromoter PRIMO on the Dnajc22-associated gene set → is Hnf4a among the top predicted TFs (paper Fig. 1d: pcaGoPromoter → Hnf4a, Hnf1b, Nr1h2, Hnf1a)?
Out of scope (not attempted, why)
- SOM subcluster size = 225 genes (mouse): the shipped SOM block has
placeholder bugs (
som.input2is undefined;genename = "Creld1"instead of Dnajc22; hard-coded cluster indices\b3\b,\b7\b). Result is highly sensitive to seed/scaling/training-subset and the exact (unshipped) variable state. The hard ~20% → documented, not chased. - iRegulon predictions (Hnf4a, Hnf1b, Gata1, Cdx2): iRegulon is a Cytoscape GUI plugin, not in the repo, not scriptable headless → external.
- WGCNA / BioLayout Express 3D network (464 nodes, 5,834 edges): BioLayout is a GUI tool; not shipped → external.
- TFBIND / PROMO / MATCH binding-site screens; ChIP-qPCR, luciferase, fly/zebrafish wet-lab validation → manual/we
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.
The paper's central computational claim — a strong cross-species co-expression between Dnajc22/DNAJC22 and Hnf4a/HNF4A — reproduces 1:1 on the authors' own pipelines and public/shipped data (mouse r=0.95, human r=0.99, with Hnf4a/HNF4A the single top correlate genome-wide in both species). The only deviations are on the periphery and are minor or on the authors' side: the operative r is never printed numerically (heatmap-only, hence an indirect endpoint, q2 yellow), a trivial 103-vs-104 sample off-by-one (q3 yellow), and a buggy shipped SOM code block that leaves the secondary 225-gene cluster / pcaGoPromoter claims unverified. A DESeq2 re-normalisation from the shipped raw counts matches the shipped normalised table, so there is no fabrication signal; severity is negligible and the core conclusion holds fully.
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.