Identification of key genes in chickpea transcriptomics and the development of ChickpeaOmicsR as a comprehensive resource to advance breeding and genomic studie
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 results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- Nothing in this column.
- 🟡Could not use the authors’ exact input data
- 🟡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 to verify the resource, NOT to reproduce every reported number 1:1. ChickpeaOmicsR is a resource package that ships the paper's processed pipeline outputs (8 .RData); I re-derived the reported numbers from those shipped objects on «our HPC» rather than re-running HISAT2/HTSeq/DESeq2 on ~205 SRA samples (the package ships the counts, so a raw re-run would not be a 1:1 check and is the deliberate hard-20% skip). RESULT = PARTIAL with notable flags: (1) the 3 named key genes (Fig 5C) are genuine and reproduce 1:1 including exact protein annotations; annotation gene count (24,681) is exact and GWAS gene count (1,050 vs 1,052) within tolerance; the Fig 5C 'five stress conditions' claim holds at FDR<0.05 (2/3 genes in 5 conditions). (2) BUT the headline count-matrix dimension quoted in the README, R/data.R and the paper (28,891 genes x 197 samples) does NOT match the actual shipped CaExpressionCounts (24,702 x 200) -- and no shipped object anywhere contains 28,891 genes; sample count is stated three different ways (197/200/205). (3) The Fig 3A intersection counts (2,910 shared) do not reproduce (got 4,348). These discrepancies are flagged as data-version-drift / possible-fabrication for human audit; the genuine named genes argue against wholesale fabrication. NOT attempted: raw SRA->counts pipeline, PPI 500-node subnetwork selection (unspecified), GWAS GEMMA reanalysis (raw genotypes not shipped), GO enrichment recompute (precomputed table shipped).
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.
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v1 current initial assessment Score 56assessed: 2026-06-14 ⛓ ceb2803156da
✎ 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
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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: opusThe study aims to identify genetic mechanisms underlying chickpea (Cicer arietinum L.) responses to diverse biotic and abiotic stressors via transcriptomic profiling, and to develop ChickpeaOmicsR, an integrated R package resource for multi-omics chickpea analysis.
- ★ ChickpeaOmicsR is the first comprehensive/specialized R package integrating transcriptomic, genomic, and proteomic (RNA-seq, GWAS, PPI) data within a unified, reproducible framework and standardizing fragmented chickpea gene nomenclature. resource
- ★ Each of the six stress/developmental conditions (drought, heat, cold, salinity, Fusarium, developmental stages) triggers distinct molecular pathways. finding
- ★ Drought and heat stress affected cell wall organization and defense responses. finding
- ★ Cold stress influenced circadian rhythm genes. finding
- ★ Fusarium stress involved pathways related to innate immunity and secondary metabolism. finding
- ★ Developmental stages showed the highest transcriptome variability among the conditions tested. finding
- An RNA-seq pipeline (fastp/FastQC QC, HISAT2 alignment, HTSeq counts, DESeq2 DEGs, STRING GO/KEGG/PPI) was used to identify stress-responsive genes. method
- GWAS integration links genetic variants to agronomic traits to support candidate gene identification in chickpea breeding. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (differential gene expression) | Cicer arietinum (chickpea), multiple tissues under heat, cold, salinity, drought, Fusarium wilt, and developmental stages | abiotic/biotic stress vs. control (heat, cold, salinity, drought, Fusarium) and developmental stage comparison | gene-level read counts and differentially expressed genes (FDR<0.01) | NCBI SRA datasets; fastp, FastQC, HISAT2, HTSeq, DESeq2 v1.38.3 |
| Gene Ontology / KEGG functional enrichment | chickpea candidate genes (Arabidopsis thaliana as annotation model) | none | enriched GO terms and KEGG pathways | STRING database |
| protein-protein interaction (PPI) network analysis | chickpea proteins (Arabidopsis thaliana annotation model) | none | protein interaction network (nodes/edges) | STRING database; Cytoscape |
| GWAS (genome-wide association study) | 679 ICARDA chickpea accessions across 11 environments over 2 years | none (natural genetic variation; agronomic traits e.g. DTF, PH, DTM, PPP, HSW, YPP) | marker-trait associations (p<0.001) | vcf2gwas with GEMMA (MLM Q+K); PLINK PCA; TASSEL filtering |
- – Drought and heat stress affected cell wall organization and defense response pathways
- – Cold stress influenced circadian rhythm genes
- – Fusarium stress involved innate immunity and secondary metabolism pathways
- – Developmental stages showed the highest transcriptome variability among tested conditions
- – Genotyping of 679 accessions yielded ~4 million SNPs, filtered to 92,821 SNPs (MAF>0.05, missing<0.1) 92,821 SNPs from ~4 million
- – Integrated expression dataset compiled covering 28,891 genes across 197 samples (CaExpressionCounts) 28,891 genes; 197 samples
- count 92,821 SNPs (SNPs retained after TASSEL filtering (MAF>0.05, missing<0.1) from 679 chickpea accessions)
- count ~4 million SNPs (SNPs from NGS genotyping before filtering)
- count 28,891 genes across 197 samples (CaExpressionCounts gene expression matrix in ChickpeaOmicsR)
- pvalue FDR <0.01 (significance threshold for DEGs (Benjamini-Hochberg))
- pvalue p < 0.001 (significance threshold for GWAS marker-trait associations)
- count 15 experiments / 6 stress conditions (RNA-seq experiments analyzed across drought, heat, cold, salinity, Fusarium, developmental stages)
- other 530.8 Mb genome assembly, 120.0x coverage (chickpea genome assembly from NCBI)
- count 52 samples (28 control vs 24 heat) (heat stress dataset PRJNA748749)
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 study performed RNA-seq-based differential gene expression analysis on 15 publicly available chickpea datasets spanning six stress conditions and developmental stages, using DESeq2 (v1.38.3) with Benjamini-Hochberg FDR correction (threshold FDR <0.01). Gene ontology, KEGG pathway enrichment, and protein-protein interaction analyses were conducted via the STRING database using Arabidopsis thaliana as an annotation model. A separate genome-wide association study was conducted on 679 accessions genotyped at 92,821 SNPs using a mixed linear model (Q+K model in GEMMA via vcf2gwas), with population structure (first two PCs) and a kinship matrix included as covariates. Results were reported primarily as lists of significant DEGs and putative trait-associated SNPs, with variance-stabilized expression values visualized in heatmaps.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 Wald test with Benjamini-Hochberg FDR adjustment | Differential gene expression between stress and control conditions across 15 RNA-seq datasets (six stress types and developmental stages) | Varies by dataset: 4–52 samples per experiment as listed in Table 1; analyses restricted to genes with mean count >10 across all samples | stated |
| Mixed linear model (Q+K model, GEMMA via vcf2gwas) | Genome-wide association analysis for 11 agronomic traits | 679 chickpea accessions, 92,821 SNPs after filtering (MAF >0.05, missing rate <0.1) | stated |
| Functional enrichment analysis (GO and KEGG) via STRING database | Gene ontology and pathway analysis of candidate DEG sets per stress condition | — | not stated |
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Each of the 15 RNA-seq datasets was analyzed independently with DESeq2, yielding a separate DEG list per dataset even when multiple datasets belonged to the same stress condition↳ Could also: Datasets from the same stress condition could be jointly analyzed after batch-effect correction (e.g., ComBat-seq or limma's removeBatchEffect), or combined via a formal meta-analysis framework (e.g., MetaDE, RankProd, or Fisher's combined p-value method) — Joint or meta-analytic approaches pool replication across studies, increasing statistical power for low-replicate datasets (n=2 per group in several experiments) and yielding a single consensus gene list per condition that quantifies cross-study concordance
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GWAS significance was assessed with a nominal threshold of p <0.001 without a genome-wide multiple-testing correction across the 92,821 SNPs tested↳ Could also: A Bonferroni-corrected genome-wide threshold (0.05/92,821 ≈ 5.4×10⁻⁷) or a permutation-based FDR threshold could also be applied — Genome-wide corrections explicitly account for the number of simultaneous hypothesis tests; the authors' relaxed threshold is described as an intentional choice for downstream integrative use, and noting the standard alternative helps readers calibrate confidence in individual locus-trait associations
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Protein-protein interactions were inferred using the STRING database with Arabidopsis thaliana as the annotation model organism↳ Could also: A legume-specific PPI resource (e.g., LegumeIP, SoyBase) or an ortholog-transfer approach mapping chickpea proteins to a more closely related species could also be used — Using a more phylogenetically proximate species or a chickpea-native interaction network reduces the potential for false interactions introduced by evolutionary divergence between Arabidopsis and Cicer arietinum
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Population structure in the GWAS model was controlled by including only the first two principal components as fixed-effect covariates↳ Could also: A larger number of PCs (selected by, e.g., Tracy-Widom test or a scree-plot elbow criterion) or a model-based Q matrix (e.g., from STRUCTURE or ADMIXTURE) could also be incorporated — The optimal number of PCs depends on the complexity of population stratification; in diverse genebank collections such as the ICARDA panel, more than two PCs may be needed to fully account for fine-scale structure and further reduce confounding
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Heatmap clustering used Pearson correlation distance with complete linkage on rlog-transformed counts↳ Could also: Spearman rank correlation distance or Euclidean distance on z-scored values could also be used for hierarchical clustering — Spearman correlation is less sensitive to outlier genes with very high variance; Euclidean distance directly reflects expression-level magnitude differences, which can be informative when the scale of fold change is itself of biological interest
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The DEG significance threshold was set at FDR <0.01 across all datasets and stress conditions↳ Could also: The more common FDR <0.05 threshold used in many transcriptomics studies, or an additional fold-change filter (e.g., |log2FC| ≥1), could also be applied — FDR <0.05 is the default in DESeq2 documentation and widely used as a reference standard; combining an FDR threshold with a minimum fold-change cutoff is also common practice to focus on genes with both statistical significance and a meaningful expression difference
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.
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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-41909810 (ChickpeaOmicsR)
Paper: Identification of key genes in chickpea transcriptomics and the development of ChickpeaOmicsR as a comprehensive resource. Front Bioinform 2026. DOI 10.3389/fbinf.2026.1727493. PMID 41909810.
Code: https://github.com/AlsammanAlsamman/ChickpeaOmicsR
(commit 733ff71f4f5ee99f58c3649d9eceeb0bf40b3e61, pushed 2024-12-04).
An R resource package that ships the processed pipeline outputs as 8 .RData
objects (expression counts, DE significance, annotation, PPI network, GWAS, etc.).
Data: 15 RNA-seq experiments from SRA (PRJNA748749 is the Heat dataset; the package integrates 14 PRJNA studies). Raw reads NOT in repo; the derived count matrix and DE tables are shipped.
Pipeline behind each reported result
- Raw reads → fastp/fastq-quality-filter → HISAT2 (ref ASM33114v1) → HTSeq counts → DESeq2 v1.38.3 DE (FDR<0.01) → igraph PPI subnetwork + GO/GWAS join.
In scope (reproduced by re-deriving from the shipped objects — "third-party
/own tool on the paper's own data", per brief rule P16/§2)
- Dimensions of the shipped resource vs the numbers stated in the paper/README (CaExpressionCounts, metadata, annotation, GWAS gene counts). [C1–C4]
- The 3 named key/hub genes (Fig 5C) — presence + protein-annotation identity. [C5]
- Hub-gene cross-condition frequency (Fig 5C "five stress conditions"). [C6]
- Per-condition DEG counts (Table 3) recomputed from CaExpressionSignificance. [C7]
- DEG intersection across conditions (Fig 3A UpSet counts). [C8]
- Full PPI network size (context for the Fig 5C subnetwork). [C9]
OUT of scope / not attempted (the hard ~20%, brief §3)
- Full SRA→counts pipeline (HISAT2/HTSeq/DESeq2 on ~205 samples). The package ships the counts; re-running would not be a 1:1 check of a reported number and is the expensive last 20%. Skipped deliberately.
- PPI subnetwork selection (Fig 5C "500 nodes / 383 edges"): the rule for reducing the 20k-node network to 500 nodes is not specified → not reproducible.
- GWAS reanalysis (92,821 SNPs, GEMMA, 679 accessions): raw genotypes not in the repo; only the derived CaGWAS table is shipped.
- GO enrichment recompute: shipped CaProteinEnrichment is the precomputed table.
All compute ran on «our HPC» (SLURM «job»); RData stayed on «infra», only small JSON results pulled to «host».
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.
This is a resource paper whose deposited data is genuine: the three named hub genes (LOC101503501/LOC101490854/LOC101495985) reproduce 1:1 with exact protein annotations, the annotation count (24,681) is exact, and GWAS genes (1,050 vs 1,052) are within tolerance — so the central biological conclusion broadly holds and this is not wholesale fabrication. However, the headline count matrix dimension (28,891 genes × 197 samples) stated in three places matches no shipped object (actual 24,702 × 200), and Fig 3A intersection counts (2,910 vs reproduced 4,348) do not reproduce. The deviations sit on the authors'/package side (data-version drift + under-specified DEG thresholds + a value not derivable from any deposit), not our method, and are moderate in severity (structure/order-of-magnitude preserved). Net: partial, flagged for human audit of the unexplained 28,891 figure.
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.