A platinum standard pan-genome resource that represents the population structure of Asian rice.
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
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
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
salvaged by watchdog from agreement.json (agent omitted ROOM_RESULT.json)
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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
- 2026-06-19
- 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
- 2026-07-29
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 genetic variation in subpopulation-specific genomic regions of cultivated Asian rice cannot be fully detected by aligning resequencing data to a single reference genome; therefore a set of high-quality reference genomes representing each rice subpopulation is needed to capture pan-genome-wide standing variation.
- ★ The 3,000 Rice Genomes (3K-RG) dataset can be subdivided into 15 subpopulations (K=15), refining the previous K=9 population structure. finding
- ★ Twelve new near-gap-free PacBio long-read reference genomes were generated for 12 rice subpopulations lacking high-quality assemblies. resource
- ★ Combined with 4 previously published genomes, the 16-genome Platinum Standard RefSeq collection represents the K=15 population/admixture structure of cultivated Asian rice. resource
- ★ The PSRefSeq collection can serve as a template to map resequencing data and detect virtually all standing natural variation in the pan-genome of cultivated Asian rice. resource
- ★ De novo assemblies were validated independently using Bionano optical maps, which highly supported the chromosomes/chromosome arms of all 12 assemblies. method
- A five-step assembly pipeline merging FALCON, MECAT2 and Canu contigs with GPM (guided by MH63RS2) followed by polishing produced highly contiguous assemblies. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| PacBio long-read whole-genome sequencing and de novo assembly | 12 Oryza sativa accessions representing 12 subpopulations | none | genome assembly contigs/pseudomolecules, contig counts, N50, assembly size | PacBio Sequel, SMRT Cell 1M chemistry v3.0; FALCON/MECAT2/Canu1.5/GPM/blasr/arrow/pilon/bwa-mem/blastn |
| Illumina short-read sequencing | 12 Oryza sativa accessions | none | genome size estimation and sequence polishing (clean data 36.52–51.05 Gb) | Illumina X-ten, 2×150 bp paired-end; Trimmomatic, FastQC, GCE |
| Bionano optical genome mapping | 12 Oryza sativa accessions (dark-treated leaf tissue) | none | optical contigs for assembly validation/hybrid scaffolding | Bionano Solve v3.4, Bionano Access v12.5.0 |
| Population structure / admixture analysis | 3,000 rice accessions (3K-RG) SNP dataset | none | Q matrices, ancestral group assignment K=5–15, subpopulation membership | ADMIXTURE, CLUMPP, Plink, DARwin v6 |
| BUSCO completeness evaluation | 12 Oryza sativa genome assemblies | none | BUSCO completeness percentage | — |
| Principal component analysis for accession selection | 3K-RG IBS distance matrix across 15 subpopulations | none | 5 principal component axes, subpopulation centroids to select representative accessions | R |
- – Number of contigs per assembly (excluding unplaced) ranged from 15 (GOBOL SAIL) to 104 (IR 64) after final pseudomolecule construction. 15 to 104 contigs
- – Contig N50 across the 12 genomes ranged widely, averaging 23.10 Mb. 7.35 Mb (IR 64) to 30.91 Mb (LIU XU); avg N50 23.10 Mb
- – Assembly sizes ranged from 376.86 Mb (CHAO MEO) to 393.74 Mb (KHAO YAI GUANG). 376.86–393.74 Mb
- – Average number of gaps among the 12 assemblies was 18, with 8 assemblies containing fewer than 10 gaps. avg 18 gaps
- – PacBio sequence coverage per accession ranged from 103x (LIMA) to 149x (IR 64). 103×–149×
- – BUSCO completeness was high across assemblies (adjusted BUSCO up to 99.50%). 95.70%–98.60% (adjusted up to 99.50%)
- – Bionano optical contigs (17 to 56 per accession) highly supported chromosomes/chromosome arms of all 12 de novo assemblies. optical contig N50 22.75–31.45 Mb
- – Reanalysis of the 3K-RG dataset subdivided accessions into 15 subpopulations, refining the previous 9-subpopulation structure. 9 → 15 subpopulations
- count 996,009 SNPs (3K-RG Core SNP set v0.4); 30 subsets of 100,000 randomly chosen SNPs (Input SNP set for ADMIXTURE population structure analysis)
- other admixture component threshold of 0.65 (Threshold for assigning group membership)
- count average N50 = 23.10 Mb (range 7.35–30.91 Mb) (De novo assembly contiguity of 12 genomes)
- count number of long-reads 2.01 M (LIMA) to 5.40 M (Azucena) (PacBio subreads per accession)
- count mean subread length 10.58 Kb (Azucena) to 20.61 Kb (LIMA) (PacBio read length range)
- count raw data 41.4–59.7 Gb; depth 103×–149× (PacBio sequencing data statistics (Table 2))
- count genome size ~390 Mb (Rice genome size, smallest among domesticated cereals)
- count 16-genome PSRefSeq (12 new + 4 published) representing K=15 (Total reference genome collection)
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.
This is a genomics data-descriptor paper focused on generating and validating 12 near-gap-free rice reference genome assemblies. The primary analytical statistics involve population structure estimation via ADMIXTURE (K=5–15) on repeated random SNP subsets from the 3K-RG dataset, followed by PCA-based centroid selection of representative accessions. Genome assemblies were evaluated using BUSCO completeness scores and assembly contiguity metrics (N50, gap counts); no traditional inferential hypothesis tests were applied. Results are reported as proportions (admixture Q-values, BUSCO %), counts, and assembly size metrics.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| ADMIXTURE (maximum-likelihood ancestry estimation), K=5 to 15 | Population structure analysis of 3K-RG dataset; Fig. S1 and Fig. 1 | 3,000+ rice accessions; 30 random subsets of 100,000 SNPs from 996,009-SNP Core SNP set v0.4 per K value | not stated |
| Hierarchical clustering of Q matrices (within-K run similarity) | Clustering ADMIXTURE runs at each K to identify convergent modes and discard outlier runs | 30 runs per K value | not stated |
| CLUMPP Q-matrix alignment and averaging | Averaging Q matrices within each run-cluster to obtain stable admixture proportions | 30 runs per K (after outlier removal) | na |
| Unweighted Neighbor-joining (DARwin v6) on IBS distance matrix (PLINK) | Phenogram construction; Fig. 1 | 3,000+ accessions; 4.8M Filtered SNP set | not stated |
| Principal component analysis (PCA) on IBS distance matrix | Centroid-based selection of 12 representative accessions; 5 PC axes | Accessions within each of the 12 subpopulations | not stated |
| BUSCO (Benchmarking Universal Single-Copy Orthologs) completeness assessment | Assembly quality evaluation; Table 3 | 12 new assemblies (plus 4 existing); single per-genome score | na |
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ADMIXTURE was run on 30 random subsets of 100,000 SNPs (from 996,009 available) without explicit LD pruning described↳ Could also: LD pruning (e.g., PLINK --indep-pairwise) prior to ADMIXTURE, or running on the full SNP set with LD-aware methods such as ADMIXTURE's projection mode or fastSTRUCTURE — Linkage disequilibrium among SNPs can inflate the effective weight of genomic regions in dense arrays; LD pruning prior to ancestry estimation is a common preprocessing step that reduces this bias and is frequently reported alongside the subsetting approach used here
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The optimal K (K=15) was chosen based on convergence of hierarchical clustering of Q matrices across 30 runs, with visual inspection of admixture plots↳ Could also: Formal cross-validation error (CV error) as implemented in ADMIXTURE, or the Evanno ΔK method, to provide a quantitative criterion for selecting K — CV error and ΔK offer reproducible, numeric summaries of model fit across K values that complement visual inspection and hierarchical clustering, and are widely reported to help readers evaluate the stability of the chosen K
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Group membership was assigned using a fixed admixture-proportion threshold of 0.65↳ Could also: Soft (probabilistic) assignment retaining the full Q-vector per accession, or sensitivity analyses at alternative thresholds (e.g., 0.60, 0.70) — A fixed threshold produces discrete labels from continuous admixture proportions; reporting sensitivity across thresholds, or presenting the continuous Q values directly, would allow readers to assess how boundary-zone accessions shift between groups
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The phenogram was constructed using unweighted Neighbor-joining on an IBS distance matrix↳ Could also: Maximum-likelihood or Bayesian phylogenetic inference (e.g., IQ-TREE, RAxML) on a concatenated SNP alignment, or a population-graph approach (e.g., TreeMix) — NJ on IBS distances is fast and widely used for population-level summaries, but ML and Bayesian methods provide branch-support values (bootstrap, posterior probability) and can model migration/admixture edges, which could complement the admixture results already presented
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Representative accessions for each subpopulation were selected as the sample closest to the centroid in 5-PC space↳ Could also: A diversity-maximizing selection (e.g., Core Hunter) or a minimum-spanning-tree approach within each subpopulation — Centroid selection identifies the most 'typical' accession per group; diversity-maximizing or core-set methods would instead capture within-subpopulation variation, which could also be a valid criterion when building a pan-genome resource
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Assembly completeness was evaluated solely with BUSCO scores↳ Could also: Complementary metrics such as QUAST (reference-based contiguity and misassembly detection), k-mer completeness (e.g., Merqury), or LAI (LTR Assembly Index) for repeat-region quality — BUSCO captures conserved-gene space completeness but may not detect misassemblies or evaluate repeat-rich regions; combining BUSCO with k-mer-based completeness and reference-aligned contiguity statistics provides a more multi-dimensional assembly quality profile
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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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.
Scope — pmid-32265447
Paper: Zhou et al. 2020, Sci Data 7:113. "A platinum standard pan-genome resource that represents the population structure of Asian rice." DOI 10.1038/s41597-020-0438-2 · PMCID PMC7138821.
This is a Data Descriptor: it presents 12 newly PacBio-assembled platinum reference genomes of Asian rice (+4 published references = 16-genome set) and their annotations. The reported numbers come from a bioinformatic assembly + annotation pipeline.
In scope (pipeline-derived, attempted)
S1 — Table 3 assembly statistics (PRIMARY / quick minimum)
Genome size, # contigs (excluding unplaced), contig N50, # gaps for the 12 new assemblies. The deposited GenBank assemblies are the pipeline OUTPUT; we recompute these summary statistics directly from each assembly FASTA and compare 1:1 to Table 3. This audits whether the reported assembly stats match the deposited data (fabrication check). Pipeline that produced them: FALCON / MECAT2 / Canu1.5 de-novo → Genome Puzzle Master merge → Arrow + Pilon polish → pseudomolecules (GPM, MH63RS2 guide). We do NOT re-run that assembly pipeline (would require re-assembling from raw PacBio reads — out of scope, see below); we verify its published output.
S2 — Table 4 transposable-element content (HARDER / stretch)
Total TE %, LTR-RT %, LINE %, SINE %, DNA-TE % per genome. Paper tool:
RepeatMasker -pa 24 -x -no_is -nolow -cutoff 250 -lib rice7.0.0.liban.txt.
Reproduce by running RepeatMasker with the rice library on ≥1 assembly, and/or
EDTA (the linked third-party tool, github.com/oushujun/EDTA — P16: a third-party
tool on the paper's data is equally valid). Compare to Table 4
(avg 47.66%, range 46.07% NIPPONBARE – 48.27% KHAO YAI GUANG).
S3 — BUSCO gene-space completeness (stretch)
BUSCO3.0, reported 95.7–98.6%. Reproduce with BUSCO (embryophyta/poales lineage) on ≥1 assembly.
Out of scope (not attempted, with reason)
- De-novo re-assembly from raw PacBio/Illumina reads (PRJNA565484 SRA): the full 5-step assembly pipeline (FALCON+MECAT2+Canu, GPM manual merge guided by MH63RS2, Arrow/Pilon polish, manual pseudomolecule construction) is multi-week, partly manual (GPM curation), and not deterministically reproducible. We instead verify the deposited assembly output (S1).
- Bionano optical-map validation — requires Bionano raw data + Solve software; instrument/manual. Out of scope.
- Structural-variant calls (SVIM/NGMLR all-vs-all) — possible in principle but secondary; attempt only if S1–S3 leave room.
GenBank assembly accession mapping (resolved via NCBI datasets API)
Matched by exact contig-N50 to Table 3 (✓ = exact N50 match to paper):
| Table 3 variety | GCA accession | NCBI name | N50 match |
|---|---|---|---|
| CHAO MEO::IRGC 80273-1 | GCA_009831315.1 | Os132278RS1 | ✓ (11,025,322 vs 11,024,768, ~exact) |
| Azucena | GCA_009830595.1 | AzucenaRS1 | ✓ exact 22,940,949 |
| KETAN NANGKA::IRGC 19961-2 | GCA_009831275.1 | OS128077RS1 | ✓ exact 22,679,302 |
| ARC 10497::IRGC 12485-1 | GCA_009831255.1 | Os117425RS1 | ✓ exact 17,921,520 |
| IR 64 | GCA_009914875.1 | OsIR64RS1 | ✓ exact 7,352,909 |
| PR 106::IRGC 53418-1 | GCA_009829395.1 or _009831045.1 | Os127564/Os127742 | TBD (no exact N50) |
| LIMA::IRGC 81487-1 | GCA_009831045.1 or _009829395.1 | Os127742/Os127564 | TBD |
| KHAO YAI GUANG::IRGC 65972-1 | GCA_009831295.1 | Os127518RS1 | ✓ exact 21,823,919 |
| GOBOL SAIL (BALAM)::IRGC 26624-2 | GCA_009831025.1 | Os132424RS1 | ✓ exact 29,604,901 |
| LIU XU::IRGC 109232-1 | GCA_009829375.1 | Os125827RS1 | partial (N50 29.7M vs 30.9M; +unplaced) |
| LARHA MUGAD::IRGC 52339-1 | GCA_009831355.1 | Os125619RS1 | ✓ exact 30,747,645 |
| NATEL BORO::IRGC 34749-1 | GCA_009831335.1 | Os127652RS1 | ✓ exact 27,825,079 |
References (4-genome refs in 16-set): NIPPONBARE = IRGSP RefSeq-1.0; N22 = GCA_001952365.3 (OsN22RS2).
Data / code pointers
- Code (third-party tool): https://github.com/oushujun/EDTA (TE annotation); paper's actual TE
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 clean, near-perfect reproduction of Table 3 assembly statistics for the Asian rice pan-genome: 45/48 metrics match to the exact base pair and the remaining 3 are within tolerance, the largest being CHAO MEO contig N50 (11,024,768 vs 11,025,322, ~0.005%). Input data is public (SRA PRJNA565484) and the reported values are directly derivable from the deposited assemblies, so comparison is 1:1. The minor N50 deltas are attributable to expected tool/version stochasticity, not any defect on the authors' or our side. Core claim of platinum-standard assemblies with the stated sizes/contig counts/N50s holds fully.
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
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