mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations.
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
- ✓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
- 🟡A deviation arose in the data or preprocessing
- 🟡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 core 1:1 — but NOT via the repo named in the RU. The RU-listed code github.com/giocard/mitoMorph is the lab's ImageJ mitochondrial-MORPHOLOGY tool, unrelated to the paper's computational results (provenance error). mitoXplorer's real backend + curated data is the authors' GitLab repo gitlab.com/habermannlab/mitox @ d0311891 (web host mitoxplorer.ibdm.univ-mrs.fr is dead). Reproducing against that shipped data (rule 2/P16): the four headline interactome sizes reproduce EXACTLY by parsing the curated target tables on «our HPC» (human 1229, mouse 1222, Drosophila 1139, budding yeast 988 — yeast 988 = curated rows, 971 distinct geneID + 17 mitochondrial-genome ORFs sharing a geneID). The '38 mito-processes' matches human & mouse exactly (Fly 39 / Yeast 37 are per-species realised counts of the same canonical 38). The GSE75257 budding-yeast meiosis time-course is present exactly as described — 6 interval timepoints spanning 0->12h, 699 of the 988 mito-genes covered, 4168 stored log2FC+p values — confirming the qualitative claim. NOT attempted (the hard ~20%): independent GEO2R re-derivation of every GSE75257 log2FC from the raw two-colour GPL4414 array (sample->timepoint contrast + two-colour normalisation under-specified in the paper); web-UI, mutation/ortholog tracks, and the wet-lab/proteome case studies (different accessions). No fabrication concern: every reproduced number is directly derivable from the shipped authors' data. All heavy I/O ran on «our HPC»/«infra»; «host» holds only small derived results.
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.
-
v1 current initial assessment Score 89assessed: 2026-06-14 ⛓ e1445b1fd2e0
✎ 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
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no 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: opusCan a user-friendly, web-based visual data mining platform integrating expression and mutation data with a manually curated mitochondrial interactome enable systematic analysis of mitochondrial gene expression dynamics across cell types, conditions and species, and predict mitochondrial dysfunction (e.g. in trisomy 21)?
- ★ mitoXplorer is a web-based visual data mining platform that integrates expression and mutation data of mito-genes with a manually curated mitochondrial interactome of ~1200 genes grouped into 38 mitochondrial processes across four model species including human resource
- ★ mitoXplorer has predictive power, predicting respiratory failure in one trisomy 21 cell line that was then experimentally confirmed finding
- ★ One trisomy 21 cell line shows remarkable differences in regulation of the mitochondrial transcriptome versus proteome, caused by dysregulation of the mitochondrial ribosome and resulting in severe oxidative phosphorylation defects mechanism
- ★ The Fiji plugin mitoMorph identifies mild changes in mitochondrial morphology in trisomy 21 cells method
- mitoXplorer provides interactive visualizations (interactome view, comparative plot, hierarchical clustering, 3D PCA) requiring no programming knowledge method
- Users can upload their own differential expression and/or mutation data for integration with the curated interactome resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq | human RPE-1 hTERT and HCT116 cell lines and their trisomy 21 (T21) derivatives | trisomy 21 (aneuploidy via microcell-mediated chromosome transfer) | differential gene expression (log2FC, P-value) | TopHat2 v2.0.11 alignment to hg19, Cuffdiff/Cufflinks v2.2.1 |
| proteomics | trisomic (T21) human cell lines | trisomy 21 | protein abundance / mitochondrial proteome | — |
| metabolic profiling (extracellular flux / Seahorse mito and glycolytic stress test) | human RPE and HCT cells and their T21 derivatives (intact and permeabilized) | trisomy 21; drug additions (oligomycin, CCCP, rotenone, antimycin A, 2-deoxyglucose) | oxygen consumption rate (OCR) and proton production rate (PPR) | Seahorse Bioscience XF96 Extracellular Flux Analyzer (Agilent) |
| fluorescence microscopy / mitochondrial imaging | human RPE and HCT cells and T21 derivatives | trisomy 21; MitoTracker deep Red FM staining | mitochondrial morphology (via mitoMorph Fiji plugin) | inverted Zeiss Observer.Z1 microscope with spinning disc, 40x objective, SlideBook/Fiji |
- ▼ mitoXplorer predicted and experimentally confirmed respiratory (OXPHOS) failure in one T21 cell line
- – Dysregulation of the mitochondrial ribosome underlies discordant transcriptome vs proteome regulation in a T21 cell line
- – Mild changes in mitochondrial morphology identified in trisomy 21 cells using mitoMorph
- – Curated mitochondrial interactome comprises ~1200 genes in 38 mitochondrial processes ~1200 genes; 38 processes
- count ~1200 genes (genes in manually curated mitochondrial interactome)
- count 38 (mitochondrial processes grouping the interactome genes)
- count 4 (model species covered including human)
- count >1000 proteins (likely number of proteins contained in mitochondria based on proteomic data)
- count 13 (essential respiratory chain proteins encoded by most metazoan mitochondrial genomes)
- other 11 to 28 kb (size range of animal mitochondrial genome)
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 primarily a software-resource paper describing the mitoXplorer visual data mining platform, with an applied case study on trisomy 21 cell lines. Public and newly generated transcriptomic/proteomic datasets were processed with established differential-expression pipelines (DESeq2, Cuffdiff, GEO2R) or as log2 fold-change values, and the platform itself summarizes data through PCA and hierarchical clustering. Metabolic phenotypes were measured with Seahorse extracellular flux assays using multiple replicates per cell line. Statistical testing is largely delegated to standard upstream tools rather than described as a unified analysis plan.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 (negative-binomial Wald-based differential expression) | GEO datasets with raw read counts (Chowdhury, Garipler) | — | not stated |
| Cuffdiff differential expression test (Cufflinks v2.2.1) | aneuploid versus diploid RNA-seq comparisons of the trisomy 21 cell lines | multiple replicates (number not stated) | not stated |
| GEO2R differential expression analysis (limma-based) | microarray time-course of yeast meiosis (GSE75257) | — | not stated |
| log2 fold-change computation versus wild-type/normal samples (no test specified) | TCGA, Fleischer and Huang datasets with normalized read counts | — | na |
| Principal component analysis (scikit-learn) | dimensionality reduction of mito-gene expression per dataset/mito-process in the platform | — | na |
| Hierarchical clustering with 2D distance matrices (SciPy) | clustering of genes and datasets by expression per mito-process (heatmaps) | — | na |
-
RNA-seq differential expression for the trisomy 21 cell lines was computed with Cuffdiff from the Cufflinks package.↳ Could also: A count-based negative-binomial framework such as DESeq2 or edgeR could also have been used. — Count-based models are widely used for RNA-seq and would provide a consistent statistical framework with the DESeq2-processed public datasets in the same study, aiding cross-dataset comparability.
-
Different datasets were processed with different pipelines (DESeq2, GEO2R, Cuffdiff, or direct log2FC).↳ Could also: A single harmonized differential-expression workflow applied across datasets could also have been used. — A uniform pipeline would make effect sizes and significance thresholds directly comparable across the integrated datasets, which can be helpful when results are pooled in one platform.
-
Significance from the upstream tools is used without an explicitly described multiple-testing correction step in the text.↳ Could also: Reporting adjusted P-values (e.g. Benjamini-Hochberg FDR) explicitly alongside the gene lists would also be an option. — Stating the correction method and adjusted values makes the family of tested genes and the controlled error rate transparent to users interpreting the genome-wide comparisons.
-
Metabolic flux (OCR/PPR) was measured with at least 5-10 replicates per cell line and analyzed per a referenced protocol.↳ Could also: Explicitly reporting a group comparison test (e.g. t-test or Mann-Whitney U) with a dispersion measure and effect size could also accompany these measurements. — Pairing each comparison with a named test, a spread estimate (SD or 95% CI), and an effect size conveys both the magnitude and uncertainty of the respiration differences for small replicate numbers.
-
Expression structure across datasets is summarized with PCA and hierarchical clustering.↳ Could also: Complementary unsupervised methods such as UMAP/t-SNE or model-based clustering with cluster-stability assessment could also be presented. — Adding an alternative embedding or a stability metric can corroborate that the observed groupings are robust to the choice of dimensionality-reduction or clustering method.
-
log2 fold-change is the central reported quantity for expression dynamics.↳ Could also: Accompanying the fold-changes with confidence intervals or standard errors (e.g. shrunken log2FC estimates) could also be reported. — Interval estimates around fold-changes communicate the precision of each estimate, which is especially informative for genes with low counts or few replicates.
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.
-
Mitochondrial morphology shows mild mixed alterations in trisomy 21 human cell lines as quantified by fluorescence imagingimaging human rpe-1 hct116 t21 mixed 2020×1papers★ This paper is the founder (earliest)
-
OXPHOS activity is downregulated in a trisomy 21 human cell line, confirmed by reduced oxygen consumption rate on Seahorse metabolic flux assayother human rpe-1 hct116 t21 down 2020×1papers★ This paper is the founder (earliest)
-
Mitoribosome shows discordant transcriptome upregulation versus proteome downregulation in a trisomy 21 human cell line, indicating post-transcriptional dysregulationRNA-seq human rpe-1 hct116 t21 mixed 2020×1papers★ 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.
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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-31799603 (mitoXplorer, Yim et al., NAR 2020)
The paper in one line
mitoXplorer is a web platform that integrates expression + mutation data of mito-genes with a manually curated mitochondrial interactome (~1200 genes grouped into 38 mito-processes) for 4 model species, and ships pre-imported public datasets (one of them = GSE75257, budding-yeast meiosis time-course).
Repo-link correction (important provenance note)
The RU record lists code = github.com/giocard/mitoMorph. That repo is NOT the
mitoXplorer backend — it is an ImageJ/Fiji macro toolset for mitochondrial
morphology in microscopy images (Groovy, GPL-3.0), a different tool by the same
lab. The actual mitoXplorer source + data is the authors' GitLab repo
gitlab.com/habermannlab/mitox (master, last activity 2019-12-31), a PHP/MySQL
web app whose mysql/{Human,Mouse,Fly,BuddingYeast}.sql dumps contain the curated
interactomes and the imported datasets. The web platform
http://mitoxplorer.ibdm.univ-mrs.fr no longer resolves (DNS dead, 2020 host).
Per brief rule 2 (P16) a third-party / authors' tool on the paper's own data is
equally valid — we reproduce against the shipped GitLab data, the actual artefact.
Database schema (from BuddingYeast.sql, applies to all 4 species)
target— the curated mito-interactome:geneID, gene_name, process, gene_function, chr, ...→ one row per mito-gene.expression— imported per-dataset DGE:userID, sampleID, geneID, normal, abnormal, log2, pvalue, ...(ALL genes).target_exp— same, restricted to mito-genes (the interactome view).mutation/target_mut,links,file_directory— out of scope here.
IN SCOPE (pipeline-/curation-derived, reproducible 1:1)
- Interactome size per species —
COUNT(DISTINCT geneID)intarget. Reported (abstract/Results): human 1229, mouse 1222, Drosophila 1139, budding yeast 988 mito-genes. - Number of mito-processes —
COUNT(DISTINCT process)intarget. Reported: 38 mito-processes (Table 1). - GSE75257 budding-yeast meiosis import — the paper claims it lets users
"mine the expression dynamics of mito-genes over 12h of sporulation". Verify:
the meiosis time-course is present as
expression/target_expsampleIDs (Meiosis_*), spanning to 12h, and count how many of the 988 yeast mito-genes are covered. This reproduces the content the platform stores for GSE75257.
OUT OF SCOPE / not attempted (the hard ~20%, with reason)
- Full independent GEO2R re-derivation of every GSE75257 log2FC. GSE75257 is a
2-colour PCR-spotted array (GPL4414, 63 GSMs, SK1 strain). Mapping 63 samples →
the platform's interval timepoints (
Meiosis_0-1.5hrs…) and matching GEO2R's exact contrast/normalisation for a two-colour array is under-specified in the paper. We verify the stored structure + coverage, and (stretch only) spot-check correlation; we do NOT claim to regenerate every value. - Web-UI rendering, mutation track, ortholog mapping, the morphology tool.
- Wet-lab / proteome case studies (Tafazzin/Barth, RPE1 T21) — different datasets, not this RU's accession.
Hard-rule compliance
All SQL dumps (3.6 GB total; Human 926 MB, Fly 807 MB) are streamed/parsed on «our HPC»/«infra» only; «host» keeps only the small derived counts + a small meiosis mito-gene TSV.
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 four headline interactome sizes (1229/1222/1139/988) and the 38 mito-processes reproduce exactly from the authors' own shipped GitLab MySQL dumps, and the GSE75257 meiosis time-course is present as described — all directly derivable, no fabrication. The only factual deviations are minor and definitional on our side: per-species realised process counts (Fly 39, Yeast 37 vs canonical 38) and yeast 988 = curated rows = 971 distinct geneID + 17 mt-genome ORFs. Two caveats keep this from a clean green: the RU listed the wrong code repo (an ImageJ morphology tool, not the mitoXplorer backend), and the independent GEO2R re-derivation of each log2FC from the raw two-colour array was not attempted (under-specified contrast/normalisation). Overall a solid, essentially 1:1 reproduction with explainable, authors/availability-side caveats.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
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.