Identification of Key Differentially Expressed Genes in Arabidopsis thaliana Under Short- and Long-Term High Light Stress.
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 relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓Overall, the reproduction was clean
- 🟡The central claim did not (fully) hold under reproduction
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 -> EXACT 1:1 for the headline Step-1 result. AraLightMeta (commit edbbb59) is a self-contained meta-analysis that ships its own pre-computed inputs (count matrix + per-gene DEG table + GO annotation + GENIE3 edges); it does NOT re-align FASTQ. Ran the authors' own frequent-DEG classification code (AraLightMeta.R L602-632, copied verbatim into repro_step1.R) on the authors' own shipped data on «our HPC» (env paper_figures, R 4.5.2). Reproduced EXACTLY: 978 frequent DEGs (498 up / 474 down / 6 mixed) and 99 unique conditions. One documented, value-preserving deviation: the edgeR dendrogram block was omitted because it only orders conditions and does not enter these counts (verified order-invariant). NOT attempted: (1) Step-2 short/long-specific DEG counts C3a-d (the harder ~20%, L785-1730); (2) upstream HISAT2/featureCounts/edgeR DEG calling on 280 raw libraries across 21 GEO series (out of scope - repo neither ships nor runs it; the shipped tables ARE its output); (3) GRN/WGCNA cluster counts (heavy deps; GENIE3 edges are shipped pre-computed). No fabrication signal: the reported headline figures are exactly regenerable from shipped data by shipped code.
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Assessment versions
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v1 current initial assessment Score 75assessed: 2026-06-14 ⛓ 7f2e531654dc
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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
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-07-31
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 uniform transcriptomic meta-analysis of accumulated high light (HL) stress datasets in Arabidopsis thaliana distinguish short- versus long-term HL transcriptional programs and identify the key differentially expressed genes (DEGs) and transcription factors governing the long-term adaptive response?
- ★ Short- and long-term HL responses in Arabidopsis leaves are driven by distinct transcriptional programs, with duration of HL treatment as the primary factor separating transcriptomic clusters. finding
- ★ Long-term HL adaptation involves key TFs CRF3 and PTF1 (antioxidant/jasmonate signaling), ATWHY2, WHY3, and emb2746 (chloroplast/mitochondrial gene coordination), AT2G28450 (ribosome biogenesis), and AT4G12750 (methyltransferase activity). mechanism
- ★ The AraLightDEGs publicly accessible knowledge base and the AraLightMeta R pipeline were developed for searching, meta-analysis, clustering, and gene regulatory network reconstruction of HL-responsive DEGs. resource
- ★ A meta-analysis aggregating DEG frequency across many independent experiments (rather than fold changes from individual experiments) yields a robust core set of consistently regulated HL-response DEGs. method
- ★ The core set of frequently upregulated HL DEGs is enriched for ROS/oxidative stress, general stress responses, and jasmonate/hormonal pathways, while downregulated genes are linked to auxin signaling and growth/photosynthesis suppression. finding
- Leaf and seedling tissues show tissue-specific transcriptomic effects comparable to or exceeding stress-induced changes, requiring separate analysis. finding
- Two experiments (GSE251796 and PRJNA699408) exhibited pronounced batch effects and were excluded as outliers. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Bulk RNA-seq transcriptomic meta-analysis (re-processed from raw sequencing data) | Arabidopsis thaliana leaves and seedlings (photosynthetic tissues) | High light stress (500–2000 µmol·m−2·s−1; durations from minutes to 3 days) | Differentially expressed genes (DEGs), frequency of DEG occurrence, log2 fold change | — |
| Hierarchical clustering / dendrogram of transcriptomic libraries | Arabidopsis thaliana, 99 unique transcriptomic conditions (21 experiments, 280 libraries) | High light across varying intensity and duration vs control | Clustering of conditions by similarity, tissue and duration grouping | AraLightMeta R pipeline |
| Correlation analysis of aggregated transcriptomic libraries | Arabidopsis thaliana control, short-HL, and long-HL leaf and seedling samples | Short HL (≤90 min) vs long HL (2 h–3 days) vs control | Pairwise correlation coefficients between tissue/treatment groups | — |
| GO functional enrichment analysis | Arabidopsis thaliana, most frequent DEGs (≥50% of experiments) | High light | Enriched GO biological process terms for up- and down-regulated gene sets | — |
| Gene regulatory network (GRN) reconstruction | Arabidopsis thaliana leaf short- and long-term HL DEGs | High light | Key transcription factors and their target genes | AraLightMeta R pipeline |
- – Meta-analysis of 21 experiments / 58 HL conditions yielded ~218,000 DEG instances corresponding to ~19,000 unique genes. 218,000 DEG instances; 19,000 unique genes
- – Relatively low correlation between short- and long-term HL responses in leaves revealed a dynamic temporal shift in gene expression. r=0.88
- – Control leaves vs control seedlings correlation was lower than control leaves vs short-HL leaves, indicating strong tissue-specific effects. 0.94 (control leaf vs seedling) vs 0.97 (control vs short-HL leaf)
- – 978 most frequent DEGs identified (in ≥50% of experiments): 498 upregulated (median log2FC ≥0.5) and 474 downregulated (median log2FC ≤−0.5). 978 DEGs; 498 up / 474 down
- – Filtering for DEGs found in ≥5 leaf-specific experiments produced 8510 DEGs for downstream short-/long-term analysis. 8510 DEGs
- ▲ Upregulated core DEGs strongly enriched for ROS/oxidative stress, wounding/heat/cold stress, and jasmonic acid response pathways.
- ▼ Downregulated core DEGs enriched for auxin pathway, cell communication, and signaling, linked to growth and photosynthesis suppression.
- – Two experiments (GSE251796, PRJNA699408; 8 conditions) clustered as outliers with pronounced batch effects and were excluded. 2 experiments / 8 conditions
- count 21 experiments covering 58 HL conditions (39 time/intensity combinations) (Curated dataset scope)
- count ~218,000 instances of DEGs, ~19,000 unique genes (Total DEGs identified in meta-analysis)
- count 280 transcriptomic libraries; 99 unique conditions (Libraries after QC used for clustering)
- correlation 0.88 (Short- vs long-term HL response in leaves)
- correlation 0.94 (Control leaves vs control seedlings)
- correlation 0.97 (Control leaves vs short-HL treated leaves)
- count 978 frequent DEGs (498 up, 474 down) (DEGs detected in ≥50% (27) of conditions)
- fold_change median log2FC ≥0.5 (up) / ≤−0.5 (down) (Thresholds classifying up/down-regulated DEGs)
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 presents a transcriptomic meta-analysis of Arabidopsis thaliana high-light stress responses by uniformly reprocessing raw RNA-seq data from 21 independent experiments (280 libraries, 58 conditions) using a custom R pipeline (AraLightMeta). Experimental conditions were classified into short-term and long-term high-light groups via hierarchical clustering on aggregated transcriptomic libraries. DEGs were identified through frequency-based filtering (detected in at least 50% of experiments) combined with median log2 fold-change thresholding, and GO enrichment analysis was used to characterize functional categories; gene regulatory networks were then reconstructed to identify key transcription factors.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Hierarchical clustering (linkage method not stated in available text) | Classification of 99 unique transcriptomic conditions into short-term, medium-term, and long-term high-light groups (Figure 1 dendrogram) | 99 transcriptomic conditions from 21 experiments | not stated |
| Correlation analysis (Pearson or Spearman — type not stated) | Assessment of similarity between leaf and seedling transcriptomes under control, short-HL, and long-HL conditions (Figure 2C) | Aggregated transcriptomic libraries; exact n not stated | not stated |
| GO enrichment analysis (specific statistical test and background set not stated in available text) | Functional characterization of upregulated and downregulated genes among the 978 most-frequent DEGs (Figure 3) | 498 upregulated and 474 downregulated genes from 978 most-frequent DEGs | not stated |
| Frequency-based filtering with median log2FC thresholding (non-inferential criterion) | Identification of the 978 most-frequent DEGs (detected in at least 50% of 54 non-outlier conditions; median log2FC threshold ±0.5) and the 8510 DEGs retained for downstream analysis (detected in at least 5 leaf-specific experiments) | 27 experimental conditions for the 50% threshold; 5 leaf-specific experiments for the secondary filter | na |
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DEGs were identified using frequency-based filtering (detected in ≥50% of experiments) and median log2FC thresholding as the meta-analytic summary across 21 studies↳ Could also: Formal statistical meta-analysis methods such as RankProd, metaRNAseq, or Fisher's combined probability test could also integrate effect sizes and p-values from individual studies into a unified test statistic — Formal meta-analysis methods yield a single p-value with explicit heterogeneity quantification (e.g., Cochran's Q or I²), which allows researchers to distinguish genes that are consistently regulated from those showing high cross-study variability
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Correlation coefficients between tissue or condition groups (0.88, 0.94, 0.97) were reported as point estimates without measures of uncertainty↳ Could also: Bootstrap confidence intervals or Fisher z-transformation intervals could also be reported alongside the correlation point estimates — Confidence intervals around correlations convey estimation precision and allow formal comparison of whether, for example, the short-vs-long-HL correlation (0.88) is meaningfully lower than the within-tissue correlation (0.97)
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Outlier experiments were identified and excluded based on visual inspection of the dendrogram (described as 'pronounced batch effects')↳ Could also: Quantitative outlier-detection approaches such as principal variance component analysis (PVCA), median absolute deviation on inter-sample distances, or RLE plot inspection could also be used to guide and document exclusion decisions — A quantitative threshold for exclusion makes the decision reproducible and provides a numerical criterion that other researchers can apply consistently when extending the dataset
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Short-term and long-term HL groups were defined by a binary time threshold (≤90 min vs. ≥2 h), with the boundary informed by the clustering results↳ Could also: A continuous time-course model such as spline regression implemented in limma/voom or the maSigPro package could also characterize the temporal trajectory of expression without requiring a single cutoff — Continuous time-course models capture the dynamic shape of expression changes across all treatment durations, which may reveal gradual transitions that are obscured by a binary grouping
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GO enrichment results were described qualitatively as 'statistically significant' without reporting exact or adjusted p-values, enrichment statistics, or the background gene set↳ Could also: Reporting Benjamini–Hochberg FDR-adjusted p-values, fold-enrichment or odds-ratio statistics, and the explicit background gene universe (e.g., all expressed genes) would also be standard practice for enrichment analyses — Explicit FDR thresholds and effect-size measures allow readers to calibrate the strength of enrichment signals and to reproduce the analysis with alternative thresholds or background sets
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Hierarchical clustering was the sole method used to assess global structure among the 99 transcriptomic conditions, with group boundaries interpreted visually from the dendrogram↳ Could also: Complementary dimensionality-reduction methods such as PCA or UMAP, optionally combined with a quantitative cluster-validity index (e.g., silhouette width), could also be used alongside the dendrogram — PCA or UMAP provides a continuous low-dimensional layout of inter-sample distances that makes the degree of separation between temporal groups more directly quantifiable and easier to inspect for gradients or intermediate-state samples
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40869111
Paper: Bobrovskikh, Zubairova, Doroshkov (2025). Identification of Key
Differentially Expressed Genes in Arabidopsis thaliana Under Short- and
Long-Term High Light Stress. Int J Mol Sci 26(16):7790. DOI 10.3390/ijms26167790.
Code: https://github.com/av-bobrovskikh/AraLightMeta @ edbbb59 (AraLightMeta.R, 2867 lines, R).
Data: repo ships ALL inputs as zips (self-contained). GEO GSE251796 is only
one of 21 series the upstream AraLightDEGs resource aggregated.
Pipeline structure (what the repo actually does)
The repo is a meta-analysis that starts from PRE-COMPUTED inputs shipped in the repo, NOT from raw FASTQ:
aralightdegs_counts_metadata.csv— aggregated count matrix + 6 metadata rows (280 libraries / 99 conditions across 21 GEO series).aralightdegs_all_degs.csv— precomputed per-gene DEG table from the AraLightDEGs database (14,869 genes; cols incl.total_numbers= frequency,upregulated_fc/downregulated_fc= ';'-joined log2FC values).ATH_GO_GOSLIM.txt— TAIR GO annotation (external, dated 2023-12-31).edges_test_{short,long}_{leaves,seedlings}.rds— precomputed GENIE3/DIANE edges.
AraLightMeta.R does: (1) condition clustering/dendrogram, (2) frequent-DEG
classification + GO enrichment, (3) short/long-specific gene classification,
(4) WGCNA + GENIE3 gene-regulatory-network reconstruction.
IN SCOPE (pipeline-derived, reproducible from shipped data)
| id | reported result | paper loc | pipeline | difficulty |
|---|---|---|---|---|
| C1 | 978 frequent DEGs (≥50% of HL conditions): 498 up, 474 down (+mixed) | Results / Fig (GO input set) | AraLightMeta.R L602–632, from all_degs.csv |
LOW — verbatim author code, light env |
| C2 | total HL conditions / threshold used for "frequent" filter | Methods | L297–306, L624 | LOW |
| C3 | short/long-specific DEG counts: ST up 2450 / down 2954, LT up 2408 / down 2333; 4234 unique (60%), 2398 shared (34%) | Results | Step 2 (L785–1730), leaves+seedlings | MED — more complex classification |
OUT OF SCOPE (not attempted — and why)
- Upstream RNA-seq (HISAT2 2.2.1 → featureCounts 2.0.3 → edgeR 4.4.1 exactTest, FDR≤0.05 |log2FC|≥0.5) on the 280 raw libraries from 21 GEO series. The repo does NOT ship or run this; it ships the already-computed count/DEG tables. Re-deriving it would require downloading/aligning 280 libraries — far beyond the 80/20 line and not the published code's job. The shipped tables ARE the authors' output of this step.
- GRN reconstruction (WGCNA + GENIE3) detailed cluster numbers (162/64 edges,
GO clusters). Heavy deps (WGCNA, GENIE3, biomaRt online); GENIE3 edges are
shipped pre-computed (
.rds) — so the network is partly an input, not freshly derived. Optional last-20%; attempted only if C1–C3 land with budget to spare. - GO enrichment terms (clusterProfiler/org.At.tair.db): derivable from C1 gene set but term-level output is large/qualitative; we reproduce the C1 INPUT counts, which are the auditable numbers.
Primary target: C1 (+ C2, C3 if cheap). Honest 1:1 from the authors' own code on their own data.
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 reproduced headline values — 978 frequent DEGs (498 up / 474 down / 6 mixed) and 99 unique conditions — match the paper exactly, obtained by running the authors' own code (AraLightMeta.R L602-632) verbatim on the authors' own shipped input tables; this is a clean 1:1 reproduction with strong anti-fabrication evidence and one documented, value-preserving deviation (omitted order-invariant dendrogram block). The deviation here is coverage, not correctness: the short/long-term-specific DEG counts (C3a-d) and GRN/WGCNA numbers — central to the title's short-vs-long framing — were not attempted. No issue lies on the authors' or data side for what was tested; the limitation is our scope choice (80/20). Overall a solid, exact reproduction whose central conclusion is fully confirmed for Step-1 but only partially verified for the time-specific arm.
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Reproduction footprint
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