From bud formation to flowering: transcriptomic state defines the cherry developmental phases of sweet cherry bud dormancy.
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
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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 for the DOWNSTREAM pipeline; reproduced 1:1 on the strongest result and partially on the DEG count. The hierarchical clustering of Garnet DEGs into 10 clusters is EXACT: re-running the described method (1-Pearson distance on Garnet TPM, complete-linkage hclust, cutree k=10) on the authors' deposited 6683-DEG list (Additional file 2 Table S2) reproduces all ten cluster sizes (1548,989,924,884,739,648,612,156,113,70) with Adjusted Rand Index = 1.000 (every gene in the same cluster). Reference genome (P. persica v2.0), gene count (26873) and Garnet sample count (31) confirmed exactly from the deposited GSE130426 count tables. The DESeq2 DEG count (6683 'dormant vs non-dormant') is only PARTIALLY reproduced: the paper does not specify which dates/stages form the binary dormant/non-dormant grouping nor the CV-filter scale, and DESeq2 has drifted from the 2018 version (~1.18-1.20) to 1.42.0. Sweeping 3 filters x 8 plausible groupings gives DEG counts of 5969-7696 (bracketing 6683); the best matches the authors' exact list at 73% gene overlap (Jaccard 0.54), 7166 DEGs. 100% of the authors' 6683 DEGs survive my filter, so the gap is the DESeq2 statistical call, not filtering. NOT attempted: the upstream alignment (Trimmomatic/TopHat/Picard) and Table S6 mapping statistics, which would require re-running a deprecated aligner on 82 SRA FASTQ libraries + the peach genome (deposited counts were used instead); and the Cristobalina/Regina cultivars, wet-lab phenology, RT-qPCR, GO/TF enrichment, and ML flowering models (out of pipeline-DEG/clustering scope). No evidence of fabrication: the one numeric discrepancy is cluster 1 = 1549 (paper text) vs 1548 (deposited Table S2), a benign +1.
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Assessment versions
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v1 current initial assessment Score 88assessed: 2026-06-16 ⛓ c92c3340fcd4
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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 2026-06-18no 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: opusThe study asks whether fine-resolution, genome-wide transcriptomic changes throughout sweet cherry (Prunus avium L.) flower bud development define distinct dormancy stages (organogenesis, paradormancy, endodormancy, ecodormancy), whether these transcriptional signatures are conserved across cultivars with contrasted flowering dates, and whether a small set of marker genes can predict dormancy stage.
- ★ Flower buds in organogenesis, paradormancy, endodormancy and ecodormancy stages are each defined by expression of genes in specific pathways, and the transcriptional state accurately captures the dormancy state. finding
- ★ These stage-specific transcriptional changes are conserved between sweet cherry cultivars with contrasted dormancy release dates. finding
- ★ DORMANCY ASSOCIATED MADS-box (DAM), floral identity and organogenesis genes are up-regulated during pre-dormancy stages, while endodormancy is characterized by cold response, ABA and oxidation-reduction pathways. mechanism
- ★ Endodormancy is separable into two distinct transcriptional periods (Oct/Nov vs Dec), indicating dormancy is a chain of biological events rather than an on/off mechanism. finding
- ★ A model based on the transcriptional profiles of just seven genes can accurately predict the main bud dormancy stages. method
- After dormancy release, genes for cell activity, division, differentiation, transport, cell wall biogenesis and oxidation-reduction are activated during ecodormancy. finding
- Specific transcription factors (e.g. ABF2, ABI5, MADS-box AP3/AG, ERF/DREB) have over-represented targets and target promoter motifs in stage-specific gene clusters. mechanism
- 81 transcriptomes spanning bud development provide a fine-resolution time-course resource for dormancy in three cultivars. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (transcriptomics) | sweet cherry (Prunus avium L.) flower buds, cultivars 'Cristobalina', 'Garnet', 'Regina' | none (seasonal time-course sampling, 11 dates July–April) | gene expression (TPM, differential expression between dormant/non-dormant stages) | — |
| forcing assay / phenological observation | sweet cherry flower buds, three cultivars | forcing conditions | bud break percentage (50% at BBCH stage 53 = dormancy release) | — |
| differential expression analysis | 'Garnet' RNA-seq transcriptomes | none | DEGs between dormant and non-dormant bud stages | DESeq2 (adjusted p-value threshold 0.05) |
| hierarchical clustering / PCA | 'Garnet' DEGs | none | expression clusters and sample separation by stage | — |
| GO enrichment analysis | 'Garnet' DEG clusters | none | enriched biological process GO terms per cluster | topGO (classic Fisher algorithm) |
| transcription factor target / motif enrichment | peach (Prunus persica) reference regulation applied to cherry gene clusters | none | TFs and promoter motifs with over-represented targets per cluster | PlantTFDB 4.0; FIMO; hypergeometric tests (FDR) |
| predictive modelling | sweet cherry bud transcriptomes | none | prediction of main bud dormancy stage from gene expression | seven-gene transcriptional model |
- – 6683 genes differentially expressed between dormant and non-dormant bud stages in 'Garnet' 6683 DEGs
- – PCA of DEGs cleanly separates bud stages (organogenesis and paradormancy projecting together); PC1 represents dormancy strength PC1 = 41.63% variance
- – PC2 distinguishes phases before and after dormancy release PC2 = 20.24% variance
- – DEGs grouped into ten clusters with distinct expression peaks across organogenesis/paradormancy, endodormancy, and ecodormancy 10 clusters
- – PavDAM1, PavDAM3, PavDAM6 highly expressed during paradormancy/early endodormancy; PavDAM4 peaks at end of endodormancy 4 of 6 DAM genes differentially expressed
- ▲ ABA pathway genes PavABF2, PavATHB7, PavCYP707A2 and stress gene PavHVA22 highly expressed during endodormancy
- – Floral identity genes PavAGL20 and PavFD up-regulated before dormancy; PavAG and PavAP3 peak during ecodormancy
- ▼ PavGH17 (1,3-β-glucanases) and PavPDCB3 repressed during dormancy
- count 6683 differentially expressed genes (DEGs between dormant and non-dormant 'Garnet' bud stages (DESeq2, adj. p<0.05))
- count 81 transcriptomes (total RNA-seq samples across three cultivars and 11 dates)
- other 41.63% (variance explained by PC1 (dormancy strength))
- other 20.24% (variance explained by PC2 (before vs after dormancy release))
- pvalue adj. p = 7.5E-04 (***) (PavABF2 target over-representation in cluster 8)
- pvalue adj. p = 2.8E-05 (***) (PavAP3 / PavAGL15 target motif enrichment in clusters)
- count 7 genes (genes used in transcriptional model to predict dormancy stages)
- count 11 sampling dates (harvest dates spanning bud stages July to April)
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 RNA-seq time-course study profiled sweet cherry flower buds across developmental and dormancy stages and used a largely descriptive/exploratory analytical pipeline. Differentially expressed genes were identified with DESeq2 using an adjusted p-value threshold of 0.05, samples were summarized by principal component analysis, genes were grouped by hierarchical clustering, and functional interpretation relied on GO enrichment (topGO, Fisher) plus hypergeometric tests for transcription-factor target enrichment with FDR correction. A predictive model based on seven genes was then developed to classify dormancy stages.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 differential expression (Wald test, adjusted p-value < 0.05) | DEGs between dormant and non-dormant bud stages in cultivar 'Garnet' (6683 genes) | three trees (biological replicates) per sampling date; 81 transcriptomes total across cultivars | not stated |
| GO term enrichment using a classic Fisher algorithm (topGO) | biological-process GO enrichment for each of the 10 gene clusters | — | not stated |
| Hypergeometric test for over-representation of transcription-factor target genes | Table 1, enrichment of TF targets within clusters | — | not stated |
| Motif occurrence detection (FIMO) for enriched target promoter motifs | Table 2, over-represented target motifs in clusters | — | na |
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Differential expression was identified with DESeq2 using an adjusted p-value threshold of 0.05.↳ Could also: Edge-case-robust pipelines such as edgeR or limma-voom could also have been used, and a combined adjusted-p-value plus log fold-change threshold could define DEGs. — Adding an effect-size (fold-change) criterion alongside significance, or cross-checking with an alternative pipeline, can emphasize biologically larger changes and characterize robustness of the gene set.
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Genes were grouped into ten clusters using hierarchical clustering of expression profiles.↳ Could also: Model-based or soft-clustering approaches (e.g., k-means with a chosen-k criterion, or fuzzy c-means as in Mfuzz) could also have been applied. — Soft clustering allows genes to have partial membership across temporal profiles, which can be informative for continuous time-course transcriptomic data.
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GO enrichment used a classic Fisher algorithm within topGO.↳ Could also: The topGO 'elim' or 'weight' algorithms, or other tools (e.g., GSEA), could also have been used. — GO-structure-aware algorithms account for term dependency, and GSEA-style ranked analyses avoid reliance on a hard DEG cutoff, offering complementary views of pathway signal.
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The time course was analyzed primarily by stage-based comparisons, clustering, and PCA.↳ Could also: Explicit time-series / spline-based differential expression frameworks (e.g., maSigPro, ImpulseDE2) could also have been used. — Time-aware models directly leverage the ordering of the 11 sampling dates and can identify genes with specific temporal trajectories.
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Cluster expression patterns and key genes were presented using TPM values and z-scores.↳ Could also: Adding dispersion summaries (SD, IQR, or 95% CI) across the biological replicates could also accompany the displayed values. — Showing spread across replicates conveys variability and is often preferred, particularly with a small number of replicate trees per date.
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.
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ABA pathway genes PavABF2, PavATHB7, and PavCYP707A2, along with stress gene PavHVA22, are upregulated during endodormancy in sweet cherry flower buds.RNA-seq prunus avium flower bud up 2019×1papers★ This paper is the founder (earliest)
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PC2 (20.2% variance) of DEG-based PCA distinguishes transcriptomic states before and after dormancy release in sweet cherry flower buds.RNA-seq prunus avium flower bud 2019×1papers★ This paper is the founder (earliest)
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PCA of DEGs cleanly separates bud developmental stages, with PC1 (41.6% variance) representing dormancy depth and co-projecting organogenesis with paradormancy.RNA-seq prunus avium flower bud 2019×1papers★ This paper is the founder (earliest)
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DEGs in sweet cherry flower buds form ten co-expression clusters with distinct peaks across organogenesis/paradormancy, endodormancy, and ecodormancy.RNA-seq prunus avium flower bud 2019×1papers★ This paper is the founder (earliest)
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Floral identity genes PavAGL20 and PavFD are upregulated before dormancy onset, while PavAG and PavAP3 peak during ecodormancy in sweet cherry flower buds.RNA-seq prunus avium flower bud mixed 2019×1papers★ This paper is the founder (earliest)
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PavDAM1, PavDAM3, and PavDAM6 are highly expressed during paradormancy and early endodormancy, while PavDAM4 peaks at the end of endodormancy in sweet cherry flower buds.RNA-seq prunus avium flower bud mixed 2019×1papers★ This paper is the founder (earliest)
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1,3-beta-glucanase PavGH17 and plasmodesmata callose-binding protein PavPDCB3 are repressed during dormancy in sweet cherry flower buds.RNA-seq prunus avium flower bud down 2019×1papers★ This paper is the founder (earliest)
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6683 genes are differentially expressed between dormant and non-dormant stages in sweet cherry flower buds, indicating large-scale transcriptomic reprogramming across the dormancy cycle.RNA-seq prunus avium flower bud mixed 2019×1papers★ This paper is the founder (earliest)
Citation network
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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-31830909 (Vimont et al. 2019, BMC Genomics)
"From bud formation to flowering: transcriptomic state defines the cherry developmental phases of sweet cherry bud dormancy." DOI 10.1186/s12864-019-6348-z.
The pipeline (from Methods)
RNA-seq of flower buds, 3 cultivars (Cristobalina/Garnet/Regina), 11 dates each (82 GSM in GSE130426). Per the paper: FastQC -> Trimmomatic (trim) -> TopHat (map to Prunus persica peach genome v2.0; cherry has no reference) -> Picard (remove optical duplicates; this is the only "code" link in the brief, a generic third-party tool) -> per-gene raw counts + TPM -> pre-filter -> DESeq2 DEGs (padj<0.05 BH) -> hierarchical clustering of Garnet DEGs into 10 clusters (1-Pearson distance on TPM).
In scope (pipeline-derived; attempted)
GEO ships per-sample count tables (raw counts + TPM, 26873 P. persica v2.0 genes) in GSE130426_RAW.tar — i.e. the output of the upstream alignment. The deposited counts let us reproduce the downstream statistical pipeline directly:
- DESeq2 DEG calling — "dormant vs non-dormant" for Garnet, padj<0.05 -> 6683 DEGs.
- Hierarchical clustering of those DEGs into 10 clusters (sizes 1548..70).
Ground truth for both is deposited in Additional file 2 (Table S2 = the exact 6683 DEGs with cluster IDs; Table S1 = sample->stage; Table S6 = mapping stats), which makes this auditable at the gene level (overlap, ARI) rather than just by counts.
Out of scope / not attempted
- Upstream alignment (Trimmomatic/TopHat/Picard) and Table S6 mapping statistics: would require downloading 82 SRA FASTQ libraries + the peach genome and re-running TopHat (a deprecated aligner) + Picard. The processed counts are deposited and were used instead. Mapping-rate reproduction left as not-attempted.
- Wet-lab phenology (bud-break %, dormancy-release dating), RT-qPCR validation, the DorPatterns Shiny app, GO/TF-target enrichment, and the machine-learning flowering-prediction models (Additional file 3) — manual/external, not pipeline DEG/clustering.
- Cristobalina / Regina cultivars: the headline DEG+clustering result is Garnet-only; focused there.
Notes on the "code" link
The brief's code URL is github.com/broadinstitute/picard — a generic duplicate-marking tool, not an authors' analysis repo. The authors stated analysis scripts would be on GitHub "upon acceptance"; no public repo was found (searched GitHub users/repos + DorPatterns). Per P16 we reproduce by re-running the described pipeline (DESeq2 + hierarchical clustering) on the paper's own data — equally valid.
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.
Strong, partial reproduction. The central result — hierarchical clustering of Garnet DEGs into ten developmental-phase clusters — reproduces 1:1 from the deposited Table S2 set, matching every cluster size and reaching ARI = 1.000, so the core conclusion holds and there is no fabrication concern (the only blemish is a benign +1 text typo, 1549 vs deposited 1548). The single material deviation is the DESeq2 DEG count (7166 vs reported 6683, 73% overlap), which sits on our side: the paper underspecifies the binary dormant/non-dormant grouping and DESeq2 has drifted from ~v1.18 to 1.42.0, while all 6683 authors' DEGs remain real and testable. Upstream alignment and Table S6 mapping stats were not attempted. Net: solid with explainable, non-authors-defect deviations → overall yellow.
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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.