Recombination Facilitates Adaptive Evolution in Rhizobial Soil Bacteria.
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 to reproduce. Mostly 1:1. Tier-1: 4 pan-genome COUNT claims (196 strains, 22,115 ortholog groups, 4,204 core, 17,911 accessory) reproduce EXACTLY by counting the authors' shipped Proteinortho tables; core=4,204 reconfirmed from a 2nd independent product (figshare SNP matrices). Tier-2 (figshare alignments): concatenated core alignment length reproduced to within 0.02% (3,091,752 vs 3,091,179 bp); per-genospecies nucleotide diversity pi reproduced within tolerance with exact rank order and the headline 4.5x range (got 4.46x), per-gene pi matching the paper's own Table S6 at Pearson r=0.994. PARTIAL on the 3,086 filtered gene count (got 3,298) and 334,040 variable sites (got 482,240) because criterion (4) 'introgression score <=3' uses Syntenizer3000 output NOT shipped in the repo; the missing filter preferentially removes high-SNP recombinant genes, consistent with the paper's thesis. NOT attempted (hard 20%): full SRA->SPAdes->prokka annotation (1,468,264 ORFs), Proteinortho re-run, ClonalFrameML R/theta, RAxML phylogeny, McDonald-Kreitman alpha (0.07-0.39), LD r2 medians. No fabrication detected; all reproduced values are derivable from the deposited data. Compute was light (counting + popgen on shipped/figshare intermediates) so no «our HPC» SLURM job was required.
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 80assessed: 2026-06-14 ⛓ e172faada008
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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: sonnetHomologous recombination increases the efficacy of natural selection by decoupling the fate of beneficial and deleterious mutations, and thus the proportion of amino acid substitutions fixed by adaptive evolution (α) should be positively correlated with recombination rate in bacteria (Rhizobium species).
- ★ α varies from 0.07 to 0.39 across five Rhizobium species and is positively correlated with the level of recombination finding
- ★ The positive effect of recombination on α is driven both by a higher rate of adaptive evolution (ωa) and a lower rate of nonadaptive evolution (ωna) mechanism
- ★ Species/gene classes with higher recombination (lower r2) show stronger purifying selection, reflected in lower piN/piS ratios finding
- GRAPES with the GammaZero DFE model was used to estimate the distribution of fitness effects and α from folded SFS and divergence data method
- ★ Genes were split into three equally sized intragenic LD (r2)-based recombination classes per species to test association with α method
- Alternative recombination measures (R/θ and D') largely reproduce the r2-based trend between recombination and α finding
- α does not differ significantly across genomic compartments (chromosome, chromids Rh01/Rh02, plasmid Rh03), likely due to low statistical power finding
- A curated set of 3,086 core protein-coding genes from 196 genomes of 5 closely related Rhizobium species, filtered for interspecies HGT and abnormal diversity, is provided as a resource for population genetics analysis resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Site frequency spectrum (SFS) / divergence-based DFE and alpha estimation (GRAPES, GammaZero model) | 196 Rhizobium leguminosarum species-complex genomes (5 species: gsA-gsE) | none (natural population comparison) | α, ωa, ωna estimates with confidence intervals | GRAPES |
| Intragenic linkage disequilibrium (LD) decay analysis (r2) | 3,086 core genes across 196 genomes, 5 species | none | r2 decay with genomic distance, median r2 per species/gene, recombination rate proxy | — |
| Nucleotide diversity (π) estimation | 3,086 core genes across genomic compartments (chromosome, chromids Rh01/Rh02, plasmid Rh03) in 5 species | none | π (~2Neμ) per gene/compartment/species | — |
| piN/piS ratio calculation from synonymous/nonsynonymous SFS | same core gene set, 5 species | none | strength of purifying selection | — |
| Alternative recombination rate estimation (R/θ and D') | same core gene set, 5 species | none | correlation with r2 and with α | — |
| Permutation test (200 iterations) across recombination classes | same core gene set, 5 species | none | statistical significance of α difference between extreme recombination classes | — |
| Orthologous gene group identification / core genome definition | 196 Rhizobium genomes, 22,115 orthologous gene groups | none | identification of 4,204 core genes present in all strains (3,086 retained after HGT/diversity filtering) | — |
- ▲ α across species ranges from 0.07 to 0.39, increasing with recombination level 0.07-0.39
- ▲ Most recombining species (gsC) has the highest α across all outgroup comparisons; least recombining (gsD) has the lowest α in 3 of 4 comparisons gsC alpha up to 0.36; gsD as low as 0.10
- ▼ piN/piS ranks species similarly to recombination rate: more recombining species show lower piN/piS (stronger purifying selection) gsC=0.037, gsA=0.039, gsE=0.051, gsB=0.057, gsD=0.07
- – ωa increases and ωna decreases with recombination rate across most species comparisons, with roughly equal magnitude effects
- – Nucleotide diversity (π) differs up to 4.5-fold across species 4.5-fold (gsA 0.018 vs gsB 0.0045)
- – Median r2 (inverse recombination measure) ranks species: gsC < gsA < gsE < gsB < gsD gsC=0.248, gsA=0.341, gsE=0.561, gsB=0.651, gsD=1.00
- – Permutation tests confirm significant α differences between extreme recombination classes for all species except gsD P<=0.05
- – α differences across genomic compartments (chromosome vs chromids vs plasmid) are not statistically significant
- other α range 0.07-0.39 (range of α across 5 Rhizobium species)
- other piN/piS: gsC=0.037, gsA=0.039, gsE=0.051, gsB=0.057, gsD=0.07 (purifying selection strength by species, ranked by recombination)
- mean π: gsA=0.018, gsB=0.0045, gsC=0.0140, gsD=0.00512, gsE=0.008 (average nucleotide diversity by species)
- correlation Pearson's correlation between r2 and R/θ or D' ranged from 0.19 to 0.70 (agreement between recombination measures)
- other median r2: gsC=0.248, gsA=0.341, gsE=0.561, gsB=0.651, gsD=1.00 (LD-based recombination ranking of species)
- other α(gsC as focal vs gsA outgroup)=0.36 [0.33-0.39] (example pairwise α estimate with 95% CI from table 1)
- count 3,086 core genes, 3,091,179 bp alignment length, 334,040 variable sites (final filtered data set used for analysis across 196 genomes)
- count 22,115 orthologous gene groups; 4,204 core genes present in all strains (orthology results prior to HGT/diversity filtering)
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 estimates the proportion of amino acid substitutions fixed by adaptive evolution (α) and its components (ωa, ωna) across five Rhizobium species using polymorphic site frequency spectra (folded SFS) and pairwise divergence data, fitted by maximum likelihood under a GammaZero distribution of fitness effects (DFE) model selected via AIC in the GRAPES framework. Genes are partitioned into three equally-sized recombination classes per species based on mean intragenic r², and α is compared across classes within each of 20 mirror species-pair combinations. Significance of class differences is assessed by nonoverlapping 95% confidence intervals and by gene-label permutation tests (200 permutations), with all results reported as point estimates with bracketed CIs.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Maximum-likelihood DFE estimation and α/ωa/ωna estimation via GRAPES (GammaZero model fit to folded SFS plus divergence counts) | All 20 pairwise species combinations and three recombination classes within each pair; also by genomic compartment (table 2) | 3,086 core genes; per-species strain counts gsA=32, gsB=32, gsC=112, gsD=5, gsE=11 | not stated |
| AIC model selection across candidate DFE models | Selection of best-fitting DFE model (GammaZero) from multiple distribution families | — | na |
| Gene-label permutation test (200 permutations across recombination classes) | Significance of α differences between the two most extreme recombination classes for each species pair (Supplementary fig. S7) | Species-dependent gene count with ≥10 informative sites (gsA=2,453, gsB=770, gsC=2,537, gsD=257, gsE=1,161) | not stated |
| Nonoverlapping 95% confidence intervals as significance criterion | Pairwise comparisons of α across recombination classes (fig. 2) and between mirror species (table 1) | — | not stated |
| Pearson's correlation | Pairwise correlations among three recombination measures (r², R/θ, D′) across genes (Supplementary fig. S8) | — | not stated |
| πN/πS ratio | Ranking of overall purifying selection strength by species recombination level | 3,086 core genes per species | not stated |
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Significance of α differences between recombination classes was assessed using nonoverlapping 95% CIs as a criterion rather than a formal test↳ Could also: A likelihood-ratio test comparing GRAPES models fit jointly versus separately to the two extreme recombination classes, or a bootstrap-based z-test of the difference in α estimates, could also be used — Nonoverlapping CIs are a conservative heuristic that can miss true differences when intervals nearly overlap; a formal test yields an explicit p-value and is more sensitive, which is especially relevant for the low-power gsD species (n=5 strains)
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Genes were discretized into three equally-sized recombination classes based on mean intragenic r²↳ Could also: A continuous regression of α (or ωa/ωna) on mean gene-level r² — for example via weighted regression treating CI width as a precision weight — could also quantify the recombination–adaptation relationship — Discretization can mask within-class heterogeneity and the functional form of the relationship; a continuous approach would estimate an effect size per unit change in recombination and allow model comparison (linear vs. nonlinear), adding complementary information to the class-based results
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The folded SFS was used because high shared polymorphism made ancestral-state polarization unreliable↳ Could also: If a well-diverged outgroup with minimal shared polymorphism were available, the unfolded (derived-allele) SFS with a polarized McDonald–Kreitman framework could also be applied — The unfolded SFS retains directionality information that can improve DFE estimation; the authors' motivation for using the folded SFS is explicitly stated and well-grounded given the shared-polymorphism context of a recent species complex
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No multiple-testing correction was applied across 20 pairwise species comparisons and the multiple recombination class contrasts within each↳ Could also: A Benjamini-Hochberg FDR correction across all permutation p-values, or Bonferroni correction for the family of contrasts, could also be applied — With 20 species pairs and at least one contrast per pair, the familywise type-I error rate is elevated; an FDR procedure would allow readers to distinguish findings that survive correction from those that do not, without substantially reducing power compared with Bonferroni
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The single best-fitting DFE model (GammaZero) selected by AIC was used for all α estimation↳ Could also: AIC-weighted model averaging over all candidate DFE models could also be used to propagate model-selection uncertainty into the α confidence intervals — When competing models have similar AIC scores, selecting a single model underestimates uncertainty; model averaging produces CIs that reflect both sampling variance and DFE-shape uncertainty, which is particularly relevant when comparing closely spaced α estimates across recombination classes
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r² (mean intragenic linkage disequilibrium) was used as the primary recombination proxy, with R/θ and D′ evaluated as robustness checks↳ Could also: A population-scaled recombination rate ρ = 4Ner estimated by an LD-based coalescent method (e.g., LDhat, FastEPRR) could also be used as the primary recombination measure — Mean r² is jointly determined by recombination rate and Ne, whereas ρ partially separates these; using ρ as the primary measure — or as a co-variate alongside Ne — would help distinguish whether the recombination effect on α is independent of effective population size differences among species
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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Proportion of adaptive substitutions (α) is positively correlated with recombination rate across Rhizobium leguminosarum species, ranging from 0.07 to 0.39.WGS rhizobium leguminosarum up 2021×1papers★ This paper is the founder (earliest)
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Adaptive substitution rate (ωa) increases with recombination level while non-adaptive substitution rate (ωna) decreases with nearly equal magnitude in Rhizobium leguminosarum.WGS rhizobium leguminosarum mixed 2021×1papers★ This paper is the founder (earliest)
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πN/πS is lower (stronger purifying selection) in more recombining Rhizobium leguminosarum species: gsC=0.037, gsA=0.039, gsE=0.051, gsB=0.057, gsD=0.070, tracking recombination rank.WGS rhizobium leguminosarum down 2021×1papers★ This paper is the founder (earliest)
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Linkage disequilibrium (r²) decays rapidly within the first 1,000 bp across all five Rhizobium leguminosarum species, indicating substantial intragenic recombination.WGS rhizobium leguminosarum down 2021×1papers★ This paper is the founder (earliest)
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Rhizobium leguminosarum species are ranked by median r² recombination level as gsC > gsA > gsE > gsB > gsD, spanning a fourfold range (0.248 to 1.00).WGS rhizobium leguminosarum 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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — PMID 34410427 (Cavassim et al. 2021, MBE)
Title: Recombination Facilitates Adaptive Evolution in Rhizobial Soil Bacteria DOI: 10.1093/molbev/msab247 · PMCID: PMC8662638 Code: https://github.com/izabelcavassim/Rhizobium_analysis (third-party-style group repo; per P16 this is valid) Data: SRA PRJNA510726 (196 isolates) · figshare 10.6084/m9.figshare.11568894.v5 (intermediates)
Dataset
196 Rhizobium leguminosarum isolates (white-clover root nodules, DK/FR/UK), 5 genospecies (gsA 32, gsB 32, gsC 112, gsD 5, gsE 11).
Pipeline (as described in Methods)
SPAdes v3.6.2 assembly → Jigome → prokka v1.12 annotation → Proteinortho v5.16b (synteny) orthology → codon-aware clustalo v1.2.0 alignment → custom SNP calling → population genetics (π, piN/piS, SFS) → LD / recombination (ClonalFrameML) → RAxML-ng phylogeny.
In scope vs out of scope
| # | Reported result | Pipeline | Scope | Tier | How reproduced |
|---|---|---|---|---|---|
| C1 | 196 strains | — | IN | 1 | count strains_order.csv |
| C2 | 22,115 orthologous groups | Proteinortho | IN | 1 | core+accessory shipped tables |
| C3 | 4,204 core genes (all 196) | Proteinortho | IN | 1 | count GC_core_genes_test.csv, all n=196 |
| C4 | 17,911 accessory genes (≥2) | Proteinortho | IN | 1 | count GC_accessory_genes_test.csv |
| C5 | 5 genospecies / pop structure | ANI core genes | IN | 1 | cluster shipped 196×196 ANI matrix (qualitative) |
| C6 | assembly ~7.65 Mb, GC 60.75% | SPAdes/QUAST | IN | 1 | summary_quast_assemblies.csv (paper does not print these → context only) |
| C7 | 3,086 filtered genes, align length 3,091,179 bp | clustalo+filter | IN | 2 | concat figshare Group_alns (needs filtering criteria) |
| C8 | 334,040 variable sites | SNP calling | IN | 2 | count polymorphic columns in concatenated alignment |
| C9 | π per gs (gsA .018, gsB .0045, gsC .014, gsD .00512, gsE .008) | popgen | IN | 2 | per-genospecies π from SNP matrices (needs gs labels) |
| — | 1,468,264 protein-coding seqs | prokka | IN but NOT attempted | 3 | needs full annotation of 196 assemblies (hard 80%) |
| — | r²/LD medians, α (0.07–0.39), ClonalFrameML R/θ, RAxML tree | recomb. | NOT attempted | 3 | heavy; deferred (hard 80%) |
Out of scope (not attempted, by 80/20): full SRA→assembly→annotation (SPAdes/prokka on 196 genomes), Proteinortho re-run, ClonalFrameML, RAxML phylogeny, McDonald–Kreitman α estimation. These are the expensive last ~20%.
Method note
Tier-1 reproduces the paper's count claims directly from the authors' shipped pipeline-output tables — a direct audit that the published integers match the deposited intermediates. Tier-2 (popgen on figshare alignments) recomputes the flagship sequence-level numbers and is attempted opportunistically; π per genospecies additionally needs the strain→genospecies map.
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 strong reproduction: four headline pan-genome counts match exactly from the authors' own deposited tables (core=4,204 confirmed twice independently), and the population-genetics layer (alignment length 0.02%, per-genospecies π within tolerance with exact rank order and 4.46×≈4.5× range, per-gene π Pearson r=0.994 vs Table S6) is faithful. The only real deviations — C7b (3,298 vs 3,086 genes) and C8 (482,240 vs 334,040 SNPs) — sit on the input/preprocessing side and stem from the authors not depositing the Syntenizer3000 introgression-filter intermediate, so the unfiltered set over-counts exactly the high-SNP recombinant genes the paper's thesis predicts. No fabrication is indicated; the discrepancies are explainable and directionally consistent rather than substantive, so overall is yellow (solid with explainable deviations) with no q5/q7 red.
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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.