Assessing Bos taurus introgression in the UOA Bos indicus assembly.
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.
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡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? PARTIALLY. The GitHub repo (mas-agis/taurus_segment_uoa_brahman @593bf13) is 6 post-processing/plotting scripts with hard-coded Windows paths, no README/driver, and none of the intermediate inputs (vcftools freq1_*.txt, gene lists, SNV/read summary tables, .eigenvec) are shipped; the per-sample->breed mapping lives only in supplementary Table S1. So the full upstream WGS pipeline (BWA-MEM -> GATK HaplotypeCaller -> vcftools --freq2 over 112 samples x 2 cattle reference genomes, multi-TB / thousands of CPU-hours) is NOT reproducible as-shipped within feasible scope. HOWEVER the paper publishes its FINAL output as Additional file 3 (100 segments + annotated genes), which let me re-derive the paper's downstream tallies 1:1 using the shipped script's logic: 100 segments, 343/2493 windows (13.76% ~ reported 13.7%), 2226 genes, 66 positive-selection genes in 19 segments -- ALL EXACT. Fabrication check on these six: PASS (every value is derivable from the shipped supplement; reported 2226 = script's summed-per-segment count, globally-unique = 2225). NOT attempted (the hard ~20%, ~95% of compute): the breed-level NFAA/Mb means (242/95/1162/345), per-breed SNV counts, mapped/retained read counts, and t-test p-values -- they need the full unshipped upstream pipeline + Table S1, and a single-sample slice would yield no per-sample value to match 1:1, so it was deliberately skipped per the 80/20 rule. Verdict: 1:1 exact on the reproducible downstream kernel; the upstream is a docs_insufficient gap, recorded honestly rather than dropped because real auditable agreement was achievable.
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 86assessed: 2026-06-15 ⛓ ead98bc2d198
✎ 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
- 2026-06-15
- 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: opusBecause the UOA_Brahman_1 reference was built from a Brahman cow that retains ~10% Bos taurus ancestry, the authors test whether genomic regions of taurine (B. taurus) introgression can be detected within the UOA_Brahman_1 indicine assembly by comparing variant calling against it versus the taurine ARS_UCD1.2 reference.
- ★ Aligning divergent (cross-subspecies) sequence data detects substantially more SNVs than aligning to a same-subspecies reference, indicating reference/assembly bias in variant calling. finding
- ★ Regions in UOA_Brahman_1 with fewer-than-expected nearly fixed alternative allele (NFAA) SNVs when taurine cattle are aligned represent taurine-introgressed DNA in the indicine assembly. finding
- ★ Flagged taurine segments represent 13.7% of the UOA_Brahman_1 assembly. finding
- ★ Genes within the taurine-introgressed segments were previously reported under positive selection in Brahman and include candidates for feed efficiency, development, and immunity. finding
- ★ A delta-statistic method using non-overlapping 1-Mb windows of NFAA (AA >= 0.95) sites with a mu + 1.5 sd threshold identifies putative taurine-introgressed regions. method
- ★ PCA and admixture analyses of genotypes from the flagged regions support the taurine ancestry of these loci. method
- ★ The reported list of taurine segments in UOA_Brahman_1 is a resource for interpreting genomic features in future indicine cattle re-sequencing analyses. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Whole-genome short-read alignment and SNV calling | 112 cattle individuals (54 B. taurus, 58 B. indicus) from 15 breeds | alignment to two divergent reference genomes (ARS_UCD1.2 vs UOA_Brahman_1) | mapping rate and number of called SNVs | BWA-MEM v0.7.17, samtools v1.10, Picard, GATK v4.1.0 (HaplotypeCaller) |
| SNV allele frequency / NFAA window analysis | breed-pooled VCFs of taurine and indicine breeds | alignment to ARS_UCD1.2 vs UOA_Brahman_1; taurine aligned to UOA_Brahman_1 for delta calculation | count of nearly fixed alternative allele (AA >= 0.95) sites in 1-Mb windows; delta statistic | vcftools (--freq2), awk, in-house NFAA_comparisons.py |
| Template/raw VCF generation for base recalibration | 5 Indian zebu and Brahman individuals (SRR6423815, SRR4279980, SRR4280002, SRR4280008, SRR6650038) | none | ~17.5 million variant sites used as recalibration template for UOA_Brahman_1 | bcftools mpileup |
| Principal component analysis (PCA) | merged breed genotypes plus inferred Brahman haplotype, aligned to UOA_Brahman_1 | none | 5 eigenvalues from whole-genome, introgressed, and non-introgressed SNV sets | Plink v1.9 (--pca), ggplot2 in R |
| Admixture analysis | all individuals plus Brahman haplotype | none | population structure ancestry proportions at K=3 | Admixture v1.3, R |
| Functional gene annotation | taurine-introgressed regions of UOA_Brahman_1 assembly | none | genes harboured within flagged segments and their functions | — |
- ▲ B. taurus individuals aligned against UOA_Brahman_1 detected up to five million more SNVs than against ARS_UCD1.2 up to 5 million more SNVs
- ▲ B. indicus individuals aligned against ARS_UCD1.2 produced ~1.5 million more SNVs than against UOA_Brahman_1 ~1.5 million more SNVs
- – Flagged taurine segments comprise 13.7% of the UOA_Brahman_1 assembly 13.7%
- – Brahman cattle retain on average 10% B. taurus ancestry across the genome 10%
- ▼ B. taurus alignment to UOA_Brahman_1 revealed regions with fewer-than-expected counts of NFAA SNVs, interpreted as taurine DNA
- – PCA and admixture results from introgressed-region genotypes support taurine ancestry of these loci
- count up to five million more SNVs (extra SNVs when B. taurus aligned to UOA_Brahman_1 vs ARS_UCD1.2)
- count one and a half million more SNVs (extra SNVs when B. indicus aligned to ARS_UCD1.2 vs UOA_Brahman_1)
- other 13.7% (proportion of UOA_Brahman_1 assembly flagged as taurine segments)
- other 10% (average B. taurus ancestry in current Brahman population)
- count 11,135,811 SNVs (whole genome); 801,366 SNVs (introgressed); 10,334,445 SNVs (non-introgressed) (SNV sets used for PCA)
- count ~17.5 million variant sites (in-house template VCF for UOA_Brahman_1 base recalibration)
- count 9718 triallelic sites removed (filtering before Plink biallelic analysis)
- count 112 individuals (54 B. taurus, 58 B. indicus), 15 breeds (whole-genome sequence dataset)
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 observational genomics study aligned whole-genome sequencing data from 112 cattle (54 B. taurus, 58 B. indicus across 15 breeds) to two reference assemblies (ARS_UCD1.2 and UOA_Brahman_1) using a standard BWA/GATK pipeline, comparing SNV counts across alignments descriptively. The primary analytic method for identifying taurine-introgressed regions in UOA_Brahman_1 was a genome-wide scan of nearly fixed alternative allele (NFAA, alternative allele frequency ≥ 0.95) counts in non-overlapping 1-Mb windows, with putatively introgressed windows flagged when their deviation from the genome-wide mean exceeded μ + 1.5 SD. Validation was performed with principal component analysis (PCA) and ADMIXTURE (K = 3 fixed) using SNVs from the introgressed, non-introgressed, and whole-genome regions. No formal hypothesis tests or p-values are reported; results are communicated primarily as counts, percentages, and visual summaries.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Empirical window-based thresholding: Δ_i > μ(Δ) + 1.5 SD(Δ) on NFAA counts in 1-Mb non-overlapping windows | Detection of putative B. taurus-introgressed segments in UOA_Brahman_1 from the alignment of 6 B. taurus breeds | 54 B. taurus individuals pooled by breed; genome divided into ~2,700 autosomal 1-Mb windows | not stated |
| Principal Component Analysis (PCA) via PLINK --pca | Validation of introgressed vs. non-introgressed regions; 3 separate analyses (whole genome, introgressed regions, non-introgressed regions) aligned against UOA_Brahman_1 | 113 samples (112 individuals + 1 Brahman haplotype reference); 11,135,811 / 801,366 / 10,334,445 SNVs for whole/introgressed/non-introgressed sets respectively | not stated |
| ADMIXTURE model-based clustering (K = 3 fixed) | Population structure estimation from whole-genome, introgressed-region, non-introgressed-region SNVs (UOA_Brahman_1 alignment) and whole-genome SNVs (ARS_UCD1.2 alignment) | 113 samples (112 individuals + 1 Brahman haplotype) | not stated |
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Putative introgressed windows were flagged using an empirical μ + 1.5 SD threshold on Δ values, chosen after visually observing a bimodal distribution of those values↳ Could also: A Gaussian mixture model (GMM) or finite mixture model could be fitted to the bimodal Δ distribution to estimate the two components and assign each window a posterior probability of belonging to the 'depleted-NFAA' (introgressed) component — A GMM yields data-driven component membership probabilities and a reproducible, model-selection-based threshold, reducing dependence on the manually chosen 1.5 SD cut-point
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ADMIXTURE was run at a fixed K = 3 without a stated model-selection step↳ Could also: Running ADMIXTURE across a range of K values (e.g., K = 2–8) with the built-in cross-validation (CV) error procedure could be used to select the K with the lowest CV error — CV-based K selection is the standard approach recommended in the ADMIXTURE documentation and provides an objective, reproducible criterion, particularly relevant when the number of ancestry components is not known a priori
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Introgressed segments were detected using non-overlapping fixed 1-Mb windows of NFAA counts↳ Could also: Hidden Markov Model (HMM)-based local ancestry inference methods (e.g., RFMix, HAPMIX, or LAMP-LD) could be applied at the haplotype level to infer ancestry along chromosomes — HMM-based approaches model chromosomal segments as latent ancestry states, naturally detect segment boundaries without requiring an arbitrary window size, and can provide higher-resolution ancestry calls and uncertainty estimates
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Differences in SNV counts between the two reference alignments are described descriptively as raw counts and percentages for each breed↳ Could also: A paired nonparametric test (e.g., Wilcoxon signed-rank test) on per-individual or per-breed SNV counts across the two references could be used to assess whether the count differences are consistent across the dataset — A formal paired comparison would quantify how reliably the reference-bias effect generalises across individuals and breeds, complementing the descriptive count summaries
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The extent of taurine introgression was summarised as the proportion of flagged 1-Mb windows (13.7% of the UOA_Brahman_1 assembly)↳ Could also: D-statistics (ABBA-BABA test, implemented in ADMIXTOOLS or similar) using a set of B. taurus, B. indicus, Brahman, and outgroup individuals could provide a formal test and estimate of introgression magnitude — D-statistics and related f-statistics offer a hypothesis-testing framework with analytical significance assessment and can quantify introgression proportions independently, providing complementary and cross-validated evidence of gene flow
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Genome-wide FST between B. taurus and B. indicus was not computed; the study focused on per-reference NFAA counts to characterise divergence and introgression↳ Could also: Windowed FST scans between B. taurus and B. indicus breeds could also identify introgressed regions as windows with anomalously low differentiation relative to the genome-wide background — Reduced FST in otherwise highly diverged genomic backgrounds is a classical signature of recent introgression and would offer a complementary line of evidence for the same regions flagged by the NFAA approach
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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-34922445
Paper: Naji MM et al. (2021). Assessing Bos taurus introgression in the UOA
Bos indicus assembly. Genet Sel Evol 53:96. DOI 10.1186/s12711-021-00688-1.
Repo: https://github.com/mas-agis/taurus_segment_uoa_brahman
@ 593bf13a1d180ed4f78246bddeaa7c510a803d73 (master, 2021-09-28; only branch, no tags).
Data: NCBI SRA — 112 WGS individuals, 15 cattle breeds (54 B. taurus / 58 B.
indicus); per-individual accessions only in Additional file 1: Table S1.
Brief's accession SRR6423815 is one of 5 zebu used to build the "template VCF".
Nature of the shipped code (important)
The repo is 6 post-processing / plotting scripts (Python + R), NOT a runnable pipeline:
NFAA_comparisons.py(= renameduoa_vs_ars.py) — windows vcftools--freq2outputs into 1-Mb NFAA counts, Z-scores, deltas, segments, gene overlap.snpeff_bed_annotation_postprocess.py— counts genes in annotated segments.ttest_snvs_total.R— paired t-tests on SNV / read summary tables.pca_plot.r,for_alignment_details.py,slurm-out_metadata.py— plotting / metadata wrangling.
Every script reads intermediate files with hard-coded Windows paths
(«path», «path») that are NOT in the repo: the
freq1_*.txt vcftools outputs, coding_genes.txt, koufarioutis_genes_taurus.txt,
UOA_genes_full.txt, SNVs_total.txt, total_retain_groups.txt,
CV_error_admixture.txt, *.eigenvec, alignment_metadata.txt, slurm*.out.
The README in git history is the GitHub default placeholder (# cautious-octo-doodle)
— no run instructions, no driver, no sample→breed map.
In scope (pipeline-derived) and feasibility
| # | Reported result | Pipeline | Feasible? |
|---|---|---|---|
| A | 100 compacted introgressed segments; 343/2493 one-Mb windows (13.7% of genome) | segmenting logic in NFAA_comparisons.py |
YES — derivable from shipped Additional file 3 |
| B | 2226 genes in taurine regions; 66 under positive selection in 19 segments | snpeff_bed_annotation_postprocess.py |
YES — derivable from shipped Additional file 3 |
| C | NFAA/Mb means: indicus 242±96 / taurus 95±68 (vs ARS); taurus 1162±633 / indicus 345±163 (vs UOA) | BWA-MEM→GATK HaplotypeCaller→vcftools --freq2→NFAA_comparisons.py |
NO (see below) |
| D | Table 2 per-breed SNV counts; ~5M more SNVs cross-subspecies; t-tests p=0.0033/0.00069/0.53/0.0002 | full pipeline + ttest_snvs_total.R on unshipped tables |
NO |
| E | Table 1 mapped/retained read counts | full pipeline | NO |
A and B are reproduced (this room). They are the script's final outputs, and the paper ships the final segments+genes table (Additional file 3), so the counting logic can be re-run on the paper's own data — a genuine, auditable 1:1.
Out of scope / not attempted (the hard ~20%, here ~95% of compute) — and why
C, D, E require running the entire upstream pipeline over 112 WGS samples × 2
cattle reference genomes (ARS-UCD1.2 ≈2.49 Gb, UOA_Brahman_1 ≈2.48 Gb): BWA-MEM
alignment, Picard mark-dups, GATK BQSR + HaplotypeCaller (GVCF), joint genotyping,
variant filtration, vcftools --freq2. This is multi-TB of FASTQ and thousands of
CPU-hours, and as-shipped it is not runnable: (1) the per-sample→breed mapping
is only in supplementary Table S1, not the repo; (2) no alignment/calling driver
scripts are shipped (only slurm-log parsers); (3) no intermediate freq1_*.txt
files are shipped. A bounded single-sample slice was considered and rejected: the
paper reports only per-breed-group means, so a single-sample run has no
reported value to match 1:1 — it would burn heavy compute for an orphan number,
which the brief's 80/20 rule says to avoid. Therefore C/D/E are documented as
not-attempted rather than partially run.
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.
Downstream kernel reproduced 1:1: all six tallies (100 segments, 343/2493 windows, 13.7%≈13.76%, 2226 genes [2225 globally-unique, consistent with the script's summed-per-segment logic], 66 positive genes, 19 segments) are exactly derivable from the shipped Additional file 3, and the fabrication check passes. Caveats: this verification is against the paper's own final output (internal consistency, not raw-data re-derivation), and the entire upstream half (NFAA/Mb means 242/95/1162/345 and t-tests p=0.0033/0.00069, C7–C12) was not attempted because the 112-sample × 2-reference WGS pipeline is unreproducible as-shipped — only post-processing scripts with hard-coded paths and no intermediate data were deposited. The gap is on the authors' side (incomplete code/data deposit) plus pure compute-infeasibility, not a wrong value; severity of the matched claims is negligible (rounding only), so the central introgression conclusion holds while a substantial portion remains unverified.
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.