Integration of Dual Stress Transcriptomes and Major QTLs from a Pair of Genotypes Contrasting for Drought and Chronic Nitrogen Starvation Identifies Key Stress
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
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 only indirectly comparable
- 🟡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
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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 only for the trimming step. The paper's core transcriptome pipeline (read mapping + DEG calling) runs on CLC Genomics Workbench v12, a commercial license-locked product, so the mapping rate (89.91%) and all DEG counts (8926 total + the per-comparison table) are NOT 1:1 reproducible (env_unresolvable for that sub-pipeline; an open aligner+DESeq2 would be a different method and was de-prioritised per 80/20). Two things WERE faithfully checked: (1) raw-read claims from SRA/ENA metadata - 16 PE libraries resolve publicly, total ~1.10B reads vs reported 1.16B (~5% low), and the NAMED smallest/largest samples (IR64 N-W+ shoot / N22 N+W+ root) are exactly the deposit's extremes -> within-tol/partial; (2) the sickle trimming step run with the paper's EXACT stated parameters (sickle 1.33, Phred<30, len<36, Phred+33 verified) retains 82.43% of reads (56.80M/lib) vs the reported 91.37% (66.51M/lib) -> MISMATCH: the stated method loses ~2x as many reads as reported, most plausibly because Q30 is more aggressive than a 91% retention implies or the figure actually came from CLC, not sickle. NOT attempted: CLC mapping/DEGs, GO/KEGG enrichment, QTL mapping, and all wet-lab/field assays (biomass, RWC, chlorophyll, enzymes, qPCR). No fabrication asserted; the stated-parameters->reported-value link for the one reproducible computational claim does not hold, and is flagged for human audit.
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 43assessed: 2026-06-15 ⛓ e94136ff052a
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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
- 2026-06-15
- 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
- 2026-08-05
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: opusTo understand the genome-wide transcriptomic and physio-biochemical effects of combined (dual) low nitrogen and water deficit stress in rice, this study tests how two contrasting genotypes (drought-tolerant N22 and N-responsive IR64) respond to individual and dual stresses and integrates these responses with QTLs for nitrogen use efficiency to identify key stress-responsive candidate genes.
- ★ N22 performs better under dual (low N + low water) stress owing to better root architecture, chlorophyll/porphyrin synthesis and oxidative stress management finding
- ★ IR64 shows similar molecular responses to N-stress and dual stress, whereas N22's responses under these two conditions are distinctly different finding
- ★ Dual stress and individual stresses elicit largely downregulation of major metabolic pathways, but the degree of downregulation is consistently lower in N22 than IR64 under dual stress finding
- ★ A QTL hotspot on chromosome 6 contains 61 genes, five of which are DEGs (UDP-glucuronosyltransferase, serine threonine kinase, anthocyanidin 3-O-glucosyltransferase, nitrate induced proteins), serving as candidate genes for N use efficiency resource
- ★ Negative regulators of N-stress (NIGT1, OsACTPK1, OsBT) were downregulated in IR64, while in N22 OsBT was not downregulated mechanism
- ★ Integration of dual-stress transcriptomes with major QTLs from contrasting genotypes identifies key stress-responsive genes as a resource for rice improvement and functional biology method
- Genome-wide transcriptome study of combined N and water deficiency had not been previously reported in rice or other crops finding
- Using relaxed CLC workbench mapping parameters improved read mapping to 89.91% vs 47.35% in prior open-source analysis, important because N22 (aus) and IR64 (indica) differ from the Nipponbare (japonica) reference method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Bulk RNA-seq (transcriptome sequencing) | Rice seedlings, root and shoot tissues of IR64 and N22 genotypes | Low N (N-W+), low water (N+W-), dual stress (N-W-) vs optimal (N+W+) | Differentially expressed genes (DEGs) | CLC Genomics Workbench for mapping; reference genome Nipponbare |
| Biomass/morphological analysis | IR64 and N22 rice seedlings (root and shoot) | Low N, low water, dual stress vs optimal | Root/shoot length, fresh weight, dry weight | — |
| Relative water content (RWC) measurement | IR64 and N22 rice leaves | Low N, low water, dual stress vs optimal | RWC (%) | — |
| Chlorophyll and carotenoid quantification | IR64 and N22 rice leaves | Low N, low water, dual stress vs optimal | Chlorophyll A, B, total chlorophyll, carotenoid content (mg/g.fwt) | — |
| Root system architecture (RSA) analysis | IR64 and N22 rice roots | Low N, low water, dual stress vs optimal | TRS, LRS, FOLRN, SOLRN | — |
| N and C metabolizing enzyme activity assays | IR64 and N22 rice tissue | Low N, low water, dual stress vs optimal | Specific activity of NR, NiR, GS, GOGAT, GDH, PK, ICDH, CS (μmoles/mg/min or ∆OD/mg/min) | — |
| QTL mapping with SNP genotyping | 253 recombinant inbred lines (RILs) derived from IR64 × N22 | none (genetic mapping) | QTLs for seed and straw N content | 5K SNP array |
- – 8926 total DEGs identified across all treatments compared to optimal (N+W+) condition 8926 DEGs
- – Roughly double the number of DEGs found in shoot vs root tissues (e.g., IR64 shoot N-W+ 3357, N-W- 4005 vs root N-W+ 1174, N-W- 903) ~2-fold
- – 12 QTLs identified for seed and straw N content using 253 RILs and a 5K SNP array 12 QTLs
- – Chromosome 6 QTL hotspot region comprised 61 genes, of which five were DEGs 61 genes, 5 DEGs
- ▲ Root fresh weight of N22 much higher than IR64 across all conditions (e.g., 436.26 vs 170.56 mg under N+W+) 436.26 vs 170.56 mg
- – IR64 and N22 showed differential expression in 15 and 11 N-transporter genes respectively under one or more stresses; four also differentially expressed in N+W- 15 vs 11 genes
- ▲ N22 had higher Chl B under dual stress (max 0.166 mg/g.fwt), while IR64 highest Chl B under optimum (0.1418 mg/g.fwt) 0.166 vs 0.0622 mg/g.fwt (N22 max vs min)
- – Maximum NR specific activity in N22 under N+W- (0.81 μmoles/mg/min); NiR higher under N-stress (IR64 2.59, N22 1.83 under N-W+) NR 0.81; NiR 2.59/1.83 μmoles/mg/min
- count 8926 DEGs (Total DEGs across all treatments vs optimal condition)
- count 1174, 698, 903 (IR64 roots) and 1197, 187, 781 (N22 roots) under N-W+, N+W-, N-W- (DEGs in root tissues)
- count 3357, 1006, 4005 (IR64 shoots) and 4004, 990, 2143 (N22 shoots) (DEGs in shoot tissues under N-W+, N+W-, N-W-)
- count 1.16 billion raw reads; average 72.58 M per treatment across 16 treatments (RNA-seq raw read totals)
- other 89.91% reads mapped (vs 47.35% previously) (Mapping rate to reference genome with CLC workbench)
- mean Root dry weight reduction 43.47% vs 70.02% (IR64 vs N22) under N-W+ (% reduction in root dry weight)
- mean Shoot fresh/dry weight N22 657.06/140.5 mg vs IR64 369.9/95.13 mg under N+W+ (Shoot biomass under optimal conditions)
- mean RWC highest IR64 97.2% and lowest N22 84.24% under N-W+ (Relative water content)
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.
The study combined physiological/biochemical phenotyping of two rice genotypes (IR64, N22) under four input regimes (N+W+, N-W+, N+W-, N-W-) with genome-wide RNA-seq and QTL mapping in a recombinant inbred population. Phenotypic and enzyme-activity data were summarized as mean ± SE (n = 3), with significant differences between conditions/genotypes (p < 0.05) denoted by letter groupings above bars. Transcriptome reads were processed and mapped in CLC Genomics Workbench to identify differentially expressed genes relative to the optimal (N+W+) condition, and 12 QTLs for seed and straw N content were identified using 253 RILs and a 5K SNP array.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| unspecified significance test reported via letter groupings (p < 0.05) for biomass, RWC, chlorophyll/carotenoid, root architecture and enzyme-activity comparisons | Figs. 2, 3, 4, 5, 7 (genotype × stress-condition comparisons) | n = 3 | not stated |
| differential gene expression analysis (specific statistical model not stated in provided text) | identification of 8926 DEGs relative to N+W+ across tissues/treatments | — | na |
| QTL mapping (method not stated in provided text) | 12 QTLs for seed and straw N content | 253 recombinant inbred lines | na |
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The specific test behind the letter-group significance (p < 0.05) for multi-group comparisons is not named in the provided text.↳ Could also: A two-way ANOVA (genotype × condition) with a post-hoc test such as Tukey HSD could also be reported explicitly. — Naming the model and post-hoc method would make the basis of the letter groupings transparent and would jointly address the family of pairwise comparisons.
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Dispersion was summarized as mean ± SE with n = 3.↳ Could also: Standard deviation or a 95% confidence interval could also be presented, optionally alongside individual data points. — SD or a CI conveys the spread or estimation uncertainty directly, which is often informative for small sample sizes.
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DEGs were identified using CLC Genomics Workbench relative to the optimal condition.↳ Could also: Count-based frameworks such as DESeq2 or edgeR (negative-binomial models) could also be used for differential expression. — These tools provide model-based shrinkage and built-in Benjamini-Hochberg FDR control across the gene set, which complements fold-change-based calls.
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Significance is reported at a p < 0.05 threshold via letter groupings without exact p-values.↳ Could also: Exact p-values and effect-size estimates could also be reported. — Reporting exact values and effect sizes gives readers more granular information about both statistical and biological magnitude.
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The DEG analysis is described without an explicitly stated multiple-testing correction in this text.↳ Could also: An explicit FDR (e.g., Benjamini-Hochberg) statement could also accompany the genome-wide comparisons. — Stating the correction and its scope clarifies how the large family of per-gene tests was handled.
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The QTL mapping method for the 253-RIL, 5K-SNP analysis is not specified in the provided text.↳ Could also: Methods such as composite interval mapping or inclusive composite interval mapping with permutation-based LOD thresholds could also be named. — Specifying the mapping model and significance threshold makes the QTL identification reproducible and interpretable.
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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Chromosome 6 QTL hotspot for nitrogen content spans 61 candidate genes, five of which are stress-responsive DEGs in IR64 or N22 transcriptome dataother rice ril 2021×1papers★ This paper is the founder (earliest)
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12 QTLs for seed and straw nitrogen content mapped in 253-RIL IR64 × N22 population using a 5K SNP arrayother rice ril 2021×1papers★ This paper is the founder (earliest)
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N22 chlorophyll B content is higher under dual nitrogen and water stress (0.166 mg/g FW) than IR64 under any conditionother rice seedling leaf up 2021×1papers★ This paper is the founder (earliest)
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N22 root fresh weight is substantially higher than IR64 across all nitrogen and water conditions (436 vs 171 mg under optimal)other rice seedling root up 2021×1papers★ This paper is the founder (earliest)
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Nitrate reductase (NR) specific activity peaks in N22 under water deficit; nitrite reductase (NiR) activity is higher in IR64 than N22 under nitrogen starvationother rice seedling mixed 2021×1papers★ This paper is the founder (earliest)
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IR64 and N22 differentially express 15 and 11 nitrogen transporter genes respectively under nitrogen and/or water stress; four are induced by water deficit aloneRNA-seq rice seedling mixed 2021×1papers★ This paper is the founder (earliest)
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Shoot tissues accumulate approximately twice as many stress-responsive DEGs as root tissues across nitrogen and water stress treatments in both IR64 and N22RNA-seq rice seedling mixed 2021×1papers★ This paper is the founder (earliest)
Citation network
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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.
Downstream reach in the literature
2 downstream papers · 1 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
- Rice Transcriptome Analysis Reveals Nitrogen Starvat... 2021 · 7 cites
- Molecular characterization of superoxide dismutase (... 2026 · 0 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34089405
Paper: Sinha et al. (2021) Integration of Dual Stress Transcriptomes and Major QTLs from a Pair of Genotypes Contrasting for Drought and Chronic Nitrogen Starvation Identifies Key Stress Responsive Genes in Rice. Rice 14:50. DOI 10.1186/s12284-021-00487-8 · PMID 34089405 · PMCID PMC8179884.
Code link in registry: https://github.com/najoshi/sickle (the read-trimming tool). Data: GEO GSE147158 → SRA SRP253184 / BioProject PRJNA613213 (16 paired-end Illumina HiSeq 2500 RNA-seq libraries; public).
The reported computational pipeline (from Methods → "Data Analysis")
Verbatim pipeline as described in the paper:
- Trimming (OPEN, linked): "high quality reads were filtered from the raw reads by removing the low-quality reads (with Phred Score < 30 and read length < 36 bp) from 3′ and 5′ ends by the sliding window approach using sickle trimming tool [https://github.com/najoshi/sickle]."
- Mapping + DEG calling (CLOSED, COMMERCIAL): "CLC genomics workbench v.12 was used for mapping the reads and identification of differentially expressed genes (DEGs)." Mapping parameters: mismatch cost: 2, length fraction: 0.5, similarity fraction: 0.8. DEG cutoff: "FDR p value < 0.05, and log2 fold change > 2 (up), > −2 (down)", control = N+W+ within each tissue×genotype.
- Functional annotation: RAP-DB (functional descriptions); agriGO v2 (GO enrichment); KEGG mapper (pathway analysis).
- (Separate, non-transcriptome) QTL mapping: MSTATC, MAPMAKER 3.0, R/qtl, QTL Cartographer (CIM, 500 perms, LOD>3). Uses prior genotype data (Shanmugavadivel et al. 2017).
In scope vs out of scope
| Reported result | Pipeline | In scope? | Reproducible 1:1? |
|---|---|---|---|
| Raw read counts (Table 1: total 1.16B, per-lib range/avg, min/max sample) | SRA deposit | YES | from SRA/ENA metadata — cheap, no compute |
| High-quality reads after trimming (66.51M / 91.37% avg) | sickle (open, linked) | YES | YES — run sickle pe -q30 -l36 on the FASTQs («our HPC»/«infra») |
| Mapping rate 89.91% | CLC Workbench v12 (commercial, closed) | partial | NO — proprietary aligner+params; not 1:1 reproducible. Open aligner = different method only |
| DEG counts (8926 unique; per-comparison table) | CLC Workbench v12 | partial | NO — CLC's proprietary DE test; open DESeq2/edgeR ≠ CLC. Different method only |
| GO / KEGG enrichment | agriGO v2 / KEGG mapper (web) | out | downstream of CLC DEGs; not attempted |
| QTL results, biomass, RWC, enzymes, qPCR | MSTATC / wet-lab / field | out | non-pipeline / wet-lab — explicitly out of scope |
Decision
The only open-source, faithfully reproducible computational step is the sickle trimming (the exact tool linked in the registry, with exact parameters given). That is the 80/20 target:
- Tier A (no compute): confirm the dataset resolves and reproduce the raw-read Table 1 claims from SRA/ENA run metadata. → done on «host».
- Tier B («our HPC», the linked tool): download the 16 PE libraries to «infra» and run sickle with the paper's parameters → reproduce the 91.37% / 66.51M high-quality read retention. → the core reproduction; requires VPN→«our HPC».
The mapping rate and DEG counts are gated on CLC Genomics Workbench v12, a
license-locked commercial product that cannot be obtained or scripted here. Reproducing
those numbers 1:1 is not feasible (env_unresolvable for that sub-pipeline). An open
re-implementation (HISAT2/STAR + featureCounts + DESeq2/edgeR) would be a different
method, not a faithful reproduction, and is not expected to match CLC's numbers; it
is noted as optional plausibility-only and de-prioritised per the 80/20 rule.
No completeness claim. Only the sickle step + raw-read metadata are reproduced; the CLC-derived results are explicitly out of faithful reach and flagged for the human auditor.
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 dataset is fully public and matches the paper's design 1:1 (16 PE libraries; named smallest/largest samples are exactly the deposit's extremes), so this is not a data-availability defect — raw-read magnitudes only run a consistent ~5% low (1.103B vs 1.16B). The decisive issue is on the authors'/method side: the single fully open computational claim, sickle trimming retention, does not reproduce — exact linked tool (v1.33) + exact stated params yield 82.43% vs the reported 91.37% (read loss ~2x), with encoding and counting ruled out. The paper's actual scientific core (89.91% mapping, 8926 DEGs) sits behind commercial CLC v12 and is therefore untestable, so the central conclusion is neither confirmed nor refuted. No fabrication is indicated (exact sample-identity matches argue against it); overall a yellow — solid scaffolding with one real unexplained deviation and an unreproducible core.
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.