Transcriptomic Data Meta-Analysis Sheds Light on High Light Response in Arabidopsis thaliana L.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓Reported values were directly comparable
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡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; reproduces 1:1 within tolerance for the core pipeline results. Full INDEPENDENT re-run on «our HPC» (the Jun-16 «infra» work dir had been reclaimed): re-cloned metaRE @af3284b (the 2017 general tool, P16-valid third-party reproduction; build fixes: C++11->C++17, +#include <cstdint> in encodings.h, drop testthat C++ test sources, strip the unused limma/edgeR/GEOquery/Biobase GEO-loading deps), re-downloaded all 5 GEO series + TAIR10, rebuilt both conda envs, re-extracted 27628 promoters, and re-ran DE («job») + metaRE («job»). Results reproduce the prior run to the number. metaRE driven with the paper's OWN shipped DEG calls (Suppl S2) over 27628 TAIR10 1500bp promoters produced 59 enriched hexamers vs the reported 61 (within-tol), 43/61 exact canonical sequences including the light-responsive G-box CACGTG, ACGCGT, ACGTGG, ACACGT -- reproducing the paper's central novel computational claim. Per-dataset DEG counts (Table 1) reproduce once the cryptic 'step-down FDR by multiplying p-value by step of comparison' is read literally as adj_p = raw_p x rank: GSE134391 500 vs 492 with 459/492 (93%) of exact paper genes recovered (Jaccard 0.861), GSE111062 597 vs 595 with 5/6 timepoints at gene-Jaccard 0.80-0.88. Shipped supplementary tables are internally consistent (S2=5487 DEGs, 3533 distinct, 25 treatment pairs) and the two from-scratch values (27628 promoters, 2080 hexamers) matched EXACTLY -- NO fabrication observed in the in-scope set. NOT attempted/partial: GSE117296 & GSE117298 DE (deposited matrices use opaque unmappable sample IDs); GSE132626 gene-level (GEO ships only RPKM, not the raw counts the paper CPM-normalized -> counts match but gene identities diverge); C4 S3 not re-loaded this run (supplementary fetch blocked); and the explicitly external/manual results (ShinyGO GO, STRING+Cytoscape network, STREME/Tomtom motifs, 2B-PLS) which are out of scope for 1:1 reproduction. Overall: a strong, honest partial reproduction of the in-scope pipeline-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: 2026-06-16 ⛓ 2f33dee312d8
✎ 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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
-
🤖 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: sonnetThe study aims to systematically identify the genetic systems, gene network components, and candidate transcription factors involved in the high light stress response of Arabidopsis thaliana by integrating meta-analysis of transcriptomic experiments with promoter motif discovery and network reconstruction.
- ★ Meta-analysis of five transcriptomic experiments identified 1151 differentially expressed genes that compose a coordinated gene network responding to high light stress finding
- ★ The reconstructed high light response gene network (453 nodes, 1860 edges) divides into 21 functional modules representing specific (e.g., red/blue light, circadian, jasmonic acid) and non-specific (e.g., heat shock, ribosomal) response components finding
- ★ Ten significantly enriched regulatory motifs corresponding to TF families ARID, MYB, ZF-HD/HB, HB, Trihelix, WRKY, and C2H2 are present in promoter regions of high light-responsive DEGs finding
- ★ Distinct transcription factor families are associated with light intensity (MYB, REM vs. E2FDP, MADS), direction of expression change (HSF, S1Fa-like vs. homeobox, BBRBPC), and treatment duration (RAV, HSF vs. EIL, SBP) finding
- ★ An integrative bioinformatic pipeline combining GEO transcriptomic meta-analysis, motif discovery (metaRE/STREME/Tomtom), and network reconstruction (STRING/GeneOntology/Cytoscape) can systematically characterize a stress response system method
- Photoreceptor families (cryptochromes, phototropins, phytochromes, Zeitlupe, UVR8) converge on HY5, COP1/SPA complexes, and E3 ligase activity to regulate photomorphogenic gene expression mechanism
- ★ A two-block partial least squares analysis links experimental condition variables to TF family enrichment, explaining 85% of covariance via three major factors method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Meta-analysis of public transcriptomic datasets (DEG identification) | Arabidopsis thaliana L. (varying ecotypes, tissues, ages) | high light stress (varying intensity and duration) | differentially expressed genes (FDR < 0.05) | NCBI GEO DataSets |
| Transcription factor family prediction per dataset | DEG sets from individual Arabidopsis high light experiments | high light stress (varying intensity/duration) | enriched TF families per dataset, by expression direction | Plant Cistrome Database |
| Hexamer/motif enrichment and cis-regulatory element identification | promoter regions of 1151 selected DEGs | none (in silico analysis) | overrepresented hexamers and validated TF binding motifs | metaRE, STREME, Tomtom, ArabidopsisDAPv1 |
| Protein-protein interaction network reconstruction | 1151 selected DEGs (453-node network) | none (in silico analysis) | network topology: nodes, edges, functional clusters | STRING database, Cytoscape |
| Gene Ontology enrichment analysis | 1151 selected DEGs | none (in silico analysis) | overrepresented GO terms and metaterm clusters | ShinyGO |
| Two-block partial least squares (PLS) covariance analysis | 50 experimental treatments (intensity, duration, ecotype, tissue, age) | high light intensity and duration variation | covariance between experimental conditions and TF family enrichment | — |
- – 5487 DEGs identified across the meta-analysis (FDR < 0.05), of which 3533 were unique to individual datasets
- – A refined set of 1151 DEGs showed the greatest contribution to the overall high light expression pattern
- – Reconstructed gene network comprises 453 nodes and 1860 edges (main component: 382 nodes, 1813 edges), divided into 21 clusters
- – 61 overrepresented hexamers were narrowed to 10 significantly enriched TF motifs (ARID, MYB, ZF-HD/HB, HB, Trihelix, WRKY, C2H2)
- – Light intensity explains 38% of covariance; high intensity (>1000 μmol m−2 s−1) associates with MYB/REM enrichment, lower intensity (500–1000 μmol m−2 s−1) with E2FDP/MADS 38% covariance
- – Direction of expression change explains 29% of covariance; upregulated genes associate with HSF/S1Fa-like TFs, downregulated genes with homeobox/BBRBPC TFs 29% covariance
- – Treatment duration explains 18% of covariance; short exposures (<10 min) associate with EIL/SBP, long exposures (>6 h) with RAV/HSF 18% covariance
- – Most common TF families across individual DEG sets were MYB, bZIP, NAC, HB, WRKY, C2C2-DOF, and AP2/EREBP
- pvalue FDR < 0.05 (DEG significance threshold across meta-analysis)
- count 5487 DEGs (3533 unique) (meta-analysis of 5 experiments, 25 treatments, 84 samples)
- count 1151 DEGs (selected DEG set with greatest contribution to expression pattern)
- count 453 nodes, 1860 edges (main network: 382 nodes, 1813 edges) (reconstructed high light response gene network)
- count 21 clusters (functional modules within the gene network)
- count 10 motifs from 61 hexamers (TF binding motif enrichment in DEG promoters)
- other 85% total covariance explained by 3 major factors (two-block PLS analysis of conditions vs. TF composition)
- other 38%, 29%, 18% covariance (covariance attributed to light intensity, expression direction, and treatment duration respectively)
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 conducted a bioinformatic meta-analysis of five transcriptomic experiments (84 samples, 25 treatments) from NCBI GEO to identify differentially expressed genes (DEGs) in Arabidopsis thaliana under high light stress, applying FDR < 0.05 as the selection threshold across individual datasets. A two-block partial least squares (PLS) method was used to relate experimental condition descriptors (light intensity, duration, ecotype, tissue, direction of change) to transcription factor (TF) family enrichment across 50 treatments, with covariance decomposition reported as percentages per latent factor. Motif enrichment in promoter regions of 1,151 selected DEGs was assessed using the metaRE package, STREME, and Tomtom, and a protein–protein interaction network of 453 nodes and 1,860 edges was reconstructed via the String database and annotated using ShinyGO gene ontology enrichment.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| FDR-controlled differential expression (method of individual-study DE tool not named) | DEG identification within each of the five individual transcriptomic experiments | 84 samples across 25 treatments (combined across five experiments) | not stated |
| Two-block partial least squares (PLS) | Association of experimental condition descriptors (B1: intensity, duration, ecotype, tissue, direction) with TF family enrichment counts (B2) across 50 individual treatments | 50 individual experimental treatments | not stated |
| STREME motif enrichment | Identification of enriched sequence motifs in promoter regions of 1,151 selected DEGs | 1151 DEGs | not stated |
| Tomtom motif comparison | Matching STREME-discovered motifs to known TF binding motifs in the ArabidopsisDAPv1 database | 61 overrepresented hexamers from metaRE | not stated |
| ShinyGO gene ontology enrichment | Functional annotation of 1,151 selected DEGs; 33 metaterm clusters identified | 1151 DEGs | not stated |
-
The meta-analysis combined five experiments by pooling FDR-significant DEG lists and taking intersections/unions, with ranking by significance of expression change↳ Could also: A random-effects or fixed-effects meta-analysis pooling effect sizes (log fold-changes with their standard errors) across studies using a framework such as metafor (R) or a dedicated RNA-seq meta-analysis tool (e.g., MetaDE, RankProd, or MAMA) — Pooling effect sizes with weights for study-level variance accounts for between-study heterogeneity more formally and yields a unified effect estimate with confidence intervals, which can be more informative than vote-counting or intersection of significance lists when experiments differ substantially in design and power
-
The relationship between experimental conditions and TF family enrichment was modeled with two-block partial least squares (PLS), reporting covariance decomposition as percentages per latent factor↳ Could also: Canonical correlation analysis (CCA), regularized CCA, or a linear mixed model with experimental condition as a fixed factor and study as a random effect — CCA maximizes correlation (rather than covariance) between the two blocks and is widely used for paired multivariate blocks; mixed models explicitly account for non-independence among treatments from the same experiment, which may be present in this data structure
-
DEGs were selected using a single FDR threshold (< 0.05) with no fold-change filter↳ Could also: A combined filter of FDR < 0.05 and a minimum absolute log₂ fold-change (e.g., |log₂FC| ≥ 1), as is common in RNA-seq practice — A fold-change floor reduces the influence of statistically significant but biologically small changes, especially in large-n datasets where very small differences can achieve FDR < 0.05; reporting fold-change distributions would also allow readers to assess effect magnitude
-
Gene ontology enrichment of the 1,151 DEGs was performed with ShinyGO, producing 33 metaterm clusters↳ Could also: Competitive gene set enrichment methods such as GSEA (Gene Set Enrichment Analysis) or camera (limma) applied to the full ranked gene list rather than a binary DEG cutoff — Ranked-list enrichment methods use the full distribution of expression changes rather than a binary threshold, which can detect coordinated but moderate shifts in a pathway that may be missed when only strongly significant genes are tested
-
Protein–protein interaction network was reconstructed from the String database using the 1,151 DEGs as input nodes↳ Could also: A co-expression network (e.g., WGCNA) built directly from the expression data across the 84 samples, or a condition-specific network comparing high-light versus control connectivity — String-based networks reflect curated prior knowledge and are not specific to the experimental conditions studied; a co-expression network derived from the same dataset captures condition-specific co-regulation patterns and can identify modules not anticipated by existing annotations
-
Results from individual datasets were compared by intersecting and visualizing overlapping DEG sets (Venn-style; Figure 2a,b), with no formal heterogeneity statistic reported across experiments↳ Could also: Cochran's Q test or I² statistic to quantify between-study heterogeneity in log fold-change for each gene, as used in classical meta-analysis — Quantifying heterogeneity helps distinguish genes whose response is consistent across experimental conditions from those that vary with intensity or duration, which is directly relevant to this paper's goal of separating condition-specific from core responses
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.
Assessed papers, coloured by verdict. Click a node to open it.
- 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.
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.
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.