Corpus 1,272 assessed · 1,173 scored · 643 reproduced ≥75 · 168 flagged ·∅ 74.1/100
← New search

DFAST and DAGA: web-based integrated genome annotation tools and resources.

Biosci Microbiota Food Health · 2016
L1 89/100 PQI 92
Why this verdict

The main results reproduced: recomputed values matched the published ones within tolerance.

Reproduced on the brainbox compute brainarbeit.com
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q3 · Location of the main deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q4 · Cause of the deviation 🟡
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability Available & functional
Total score +2
✓ What held up
  • Reported values were directly comparable
  • Reported values are derivable from the shared data
  • Any deviation was negligible
  • The central claim held under reproduction
What did not (or only partly)
  • 🟡Could not use the authors’ exact input data
  • 🟡A deviation arose in the data or preprocessing
  • 🟡A deviation was attributed to the published material
  • 🟡Overall, the reproduction showed a material discrepancy
How its reproducibility compares
89/100
Reproducibility score
0.8 SD above mean
vs. all fields · 1173 studies
🎯 Scores higher than 77% of all assessed papers rank 246 of 1173 scored

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 1:1. The paper's reproducible computational core is its average-nucleotide-identity (ANI) analysis (Fig.3), and the Methods name the EXACT tool used (pyani, github.com/widdowquinn/pyani, Goris-2007 method). Per brief rule P16 this is an ideal third-party-tool reproduction: we ran pyani ANIb (BLAST+ blastn, 1020-nt fragments, >=30% id / >=70% coverage) on the EXACT NCBI Assembly genomes whose accessions are printed on the Fig.3 dendrogram leaves (read directly off the figure), on «our HPC» (SLURM «job», conda env built in-job). RESULTS: (1) L. gasseri (11/12 Fig-3B genomes) splits into exactly 2 subgroups with between-subgroup ANI 93.7% and within-subgroup ANI mean 99.3% -> reproduces the paper's '~93%' and '>98%'. (2) L. jensenii (14 Fig-3C genomes) splits into exactly 2 subgroups with between-subgroup ANI 88.1% and within-subgroup ANI mean 99.7% -> reproduces the paper's '~88%' and '>98%' essentially exactly. These two two-subgroup separations are the paper's HEADLINE NOVEL FINDINGS and they reproduce cleanly. (3) L. delbrueckii: 14 of the 15 subspecies-type-strain pairs fall in 97.26-98.38%, matching the paper's reported 97.2-98.4% almost exactly; the single exception is the jakobsenii(KACC13439, GCA_001263315.1) <-> delbrueckii(DSM20074, GCA_001908495.1) pair at 99.97% (near-identical). The subsp. delbrueckii assembly we used (GCA_001908495.1) was deposited ~Nov 2016 (likely AFTER the Jul-2016 paper), so the authors used a different/earlier delbrueckii genome; the near-identity may also flag a mislabeled public type-strain assembly - exactly the phenomenon DAGA was built to detect. No fabrication signal against the paper. DEVIATIONS: modern bioconda pyani 0.2.13.1 vs 2016 checkout (algorithm stable); ANIb (BLAST+) vs Goris-era blastall (sub-percent); 1 of 12 gasseri genomes (GCA_000814885.1) had no FASTA in its datasets package and was omitted (subgroup structure robust). NOT ATTEMPTED (hard 20%, see scope.md): the 704-genome all-vs-all (247,456 comparisons; exact genome set not enumerated in the paper), Prokka/DFAST annotation, Platanus de-novo assembly, CheckM grades, the web service, and the pheS/rpoA gene-identity numbers (different pipeline). All grades are PROVISIONAL - a human auditor re-derives from the checksummed matrices in reproduction/outputs/.

💻 Code ↗ 🗄 Data: PRJDB547

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.

  1. v1 current initial assessment Score 89
    assessed: 2026-06-16 ⛓ 66ce35e23043
✎ 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.

Reason for the rerun

We email you a confirmation link first. The rerun is an objective re-measurement — it cannot change the verdict in your favour, only ask us to look again.

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
2026-06-16
Rubric version
v1.0
Assessed by
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16
no 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: opus
Founding hypothesis

Can a web-based integrated annotation pipeline (DFAST) coupled with a curated genome repository (DAGA) provide consistently annotated, quality- and taxonomy-assessed lactic acid bacteria genomes, and can average nucleotide identity (ANI) reliably verify taxonomic affiliation and reveal intraspecific structure within Lactobacillus and Pediococcus?

Core claims
  • DFAST is a web-based bacterial genome annotation and DDBJ submission pipeline with integrated CheckM quality assessment and ANI taxonomic assessment. resource
  • DAGA is a genome repository containing 1,421 consistently annotated genomes covering 179 species and 18 subspecies of Lactobacillus and Pediococcus from DDBJ/ENA/GenBank and SRA. resource
  • ANI has high discriminative power to determine whether two genomes belong to the same species, with ~95% as the species threshold. method
  • ANI analysis detected and corrected mislabeled or misidentified genomes in public databases (77 mislabeled and 55 unidentified genomes reassigned). finding
  • Lactobacillus gasseri and L. jensenii each split into two previously unknown intraspecific subgroups whose between-subgroup divergence exceeds the 95% species ANI threshold. finding
  • A curated reference protein database tailored for Lactobacillus and Pediococcus was constructed to enable accurate, rapid, consistent annotation. resource
  • 28 of 32 genomes labeled 'L. casei' were in fact L. paracasei based on ANI. finding
  • Six representative strains showed anomalously high ANI indicating incongruent taxonomic positions. finding
Experimental setups
Assay System Perturbation Readout Platform
Genome annotation (Prokka-based customized pipeline) Lactobacillus and Pediococcus genomes none tRNA/rRNA/CRISPR/protein-coding gene predictions and functional annotation Prokka ver. 1.11 with customized LAB reference database
Ortholog clustering for reference database construction 81 genomes (69 Lactobacillus/Pediococcus + 12 others) none orthologous clusters (28,002) and protein/gene name assignments GET_HOMOLOGUES v1.3 (BLASTP, OrthoMCL); LaCOGs, MBGD, NCBI CDD
Average nucleotide identity (ANI) calculation 191 representative genomes; all-against-all 704 genomes none pairwise mean nucleotide sequence identity pyani (BLASTN, Goris et al. method)
Genome quality assessment all 1,421 DAGA genomes of Lactobacillus and Pediococcus none completeness and contamination via single-copy marker genes (409 for Lactobacillus, 664 for Pediococcus) CheckM v1.0.5
De novo genome assembly raw Illumina paired-end reads from SRA none draft genome sequences Platanus assembler v1.2.4 with Platanus_trim v1.0.7
Hierarchical clustering / phylogenetic analysis L. gasseri, L. jensenii, L. delbrueckii subspecies genomes none genome distance trees using (1-ANI) UPGMA clustering
Marker gene sequence comparison 185 representative genomes; L. gasseri and L. jensenii subgroups none 16S rRNA, pheS, rpoA gene identity
Key results
  • DAGA contains 1,421 genomes (1,389 Lactobacillus, 32 Pediococcus); 743 from DDBJ/ENA/GenBank and 678 assembled de novo from SRA. 1,421 genomes
  • All interspecific ANI values were below 95%; exception L. zeae vs L. casei at 94.4%. <95%; 94.4%
  • In all-against-all comparison of 704 genomes (247,456 pairs), all 239,840 interspecific ANI values <95%, while 198 of 7,616 intraspecific values were <95%. 198/7,616 intraspecific <95%
  • L. gasseri subgroups differed at ANI 93% and L. jensenii subgroups at 88%, while within-subgroup ANI was >98%. 93%; 88%; >98%
  • 77 mislabeled genomes were renamed and 55 unidentified 'Lactobacillus sp.' genomes were assigned names based on ANI; all marked Rating 1. 77 mislabeled; 55 unidentified
  • 28 of 32 'L. casei' genomes were L. paracasei (ANI >98% vs L. paracasei ATCC 25302T, <85% vs L. casei ATCC 393T). 28/32; >98%; <85%
  • Six strains showed anomalously high cross-species ANI (e.g. L. homohiochii 99.9% vs L. fructivorans; L. parakefiri 99.9% vs L. kefiri). 97.1–99.9%
  • ANI among six L. delbrueckii subspecies type strains ranged 97.2–98.4% yet hierarchical clustering separated them. 97.2–98.4%
Key statistics
  • count 1,421 genomes covering 179 species and 18 subspecies (total genomes in DAGA across Lactobacillus and Pediococcus)
  • count 247,456 pairwise comparisons (704 × 703/2) (all-against-all ANI comparison)
  • correlation ANI 93% between L. gasseri subgroups; 88% between L. jensenii subgroups; >98% within subgroups (intraspecific subgroup divergence)
  • fold_change ANI 94.4% (L. zeae vs L. casei, only interspecific exception below 95%)
  • count 239,840 interspecific ANI values <95%; 198 of 7,616 intraspecific <95% (diversity analysis of 704 genomes)
  • correlation pheS identity 96% (gasseri) and 93% (jensenii); rpoA 99% and 98% between subgroups (marker gene support for subgroup separation)
  • count 183,469 protein sequences grouped into 28,002 orthologous clusters (reference protein database construction)
  • other completeness ≥95% and contamination ≤5% (Rating 4/5); 409 and 664 markers for Lactobacillus and Pediococcus (CheckM quality rating thresholds and marker counts)

Statistical methods review

Model: opus

A 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 bioinformatics resource paper describing the DFAST annotation pipeline and the DAGA genome repository, and its quantitative analyses are descriptive and comparative rather than inferential. Taxonomic relationships were assessed mainly through pairwise average nucleotide identity (ANI), with a fixed 95% threshold used to distinguish species, and genome groupings were summarized with UPGMA hierarchical clustering on (1 − ANI) distances. Genome quality was characterized with CheckM completeness and contamination metrics and reported via a 5-grade rating and counts in tables. No formal hypothesis tests, p-values, or confidence intervals are reported.

Replicationna Sample sizeSample sizes are described as enumerated genome counts (e.g., 1,421 total genomes; 191 and 185 representative genomes; 704 genomes; 247,456 pairwise ANI comparisons); no power analysis is described GroupsGenome pairs/species and subspecies within Lactobacillus and Pediococcus Pairingna Randomization/blindingna Dispersionrange Exact p-valuesno Effect sizesno Confidence intervalsno Multiplicity correctionnone stated
Statistical tests used
Test Applied to n Assumptions
Average nucleotide identity (ANI) comparison against a fixed 95% species threshold (pyani/BLASTN, Goris et al. method) Pairwise comparisons among 191 representative genomes (Fig. 3A), all-against-all comparison of 704 genomes (N = 247,456 pairs), and detection of mislabeled/misidentified genomes (Tables 3, 4) 704 genomes giving 704×703/2 = 247,456 pairwise values; 239,840 interspecific and 7,616 intraspecific values; representative set of 191/185 genomes not stated
UPGMA hierarchical clustering on (1 − ANI) genomic distance Trees for L. gasseri (Fig. 3B), L. jensenii (Fig. 3C), and L. delbrueckii subspecies (Fig. 3D) not stated
CheckM completeness and contamination estimation from single-copy marker genes Quality rating of all genomes (Tables 1, 2); 409 markers for Lactobacillus and 664 for Pediococcus 1,421 genomes assessed na
Sequence identity comparison of marker/housekeeping genes (16S rRNA, pheS, rpoA) Corroboration of subgroup separation in L. gasseri and L. jensenii and validation of representative genomes na
Approaches that could also have been used
  • Species boundaries were assessed using ANI against a single fixed 95% threshold.
    Could also: One could also report a digital DNA-DNA hybridization (dDDH/GGDC) value or an ANI value accompanied by an explicit uncertainty band around the threshold. — A second genome-distance metric or an uncertainty range would provide an additional, independent line of evidence and convey how close borderline cases (e.g., the 94.4% L. zeae/L. casei pair) sit to the cutoff.
  • Genome groupings were summarized with UPGMA clustering on (1 − ANI) distances.
    Could also: Neighbor-joining or a model-based phylogeny (e.g., maximum likelihood on concatenated core genes) could also be used, optionally with bootstrap support values. — These approaches relax the ultrametric (constant-rate) assumption of UPGMA and can attach branch-support statistics, which helps quantify confidence in the inferred subgroups of L. gasseri and L. jensenii.
  • Intra- versus interspecific ANI distributions were described by counts and representative values relative to the 95% line.
    Could also: The two distributions could also be summarized with medians/IQRs or visualized and compared (e.g., a histogram overlap or a gap statistic). — Distributional summaries would convey the spread and degree of separation between intra- and interspecific values, complementing the threshold-based counts.
  • The subgroup separations in L. gasseri and L. jensenii were supported descriptively by pheS and rpoA identities.
    Could also: A clustering-validity index (e.g., silhouette width) or a formal multilocus/MLSA framework could also quantify the proposed subgroups. — An internal validity measure would put a number on how well-separated the subgroups are, supporting the suggestion that they 'might deserve subspecies-level differentiation.'
  • Reported identity figures (e.g., the 97.2–98.4% subspecies range) are given as point values or ranges.
    Could also: Summaries such as mean ± SD or a 95% confidence interval for within- and between-group ANI could also be reported. — Dispersion statistics would communicate the variability behind the summary values, which is often preferred when comparing closely spaced groups.
Software: DFAST / DAGA (Python 2.7.11, PostgreSQL 8.4.20, Nginx 1.8.0, Red Hat Enterprise Linux 6.7) Python 2.7.11 · pyani (ANI calculation) · BLASTN / BLASTP (NCBI BLAST) · Prokka (annotation pipeline, customized) 1.11 · GET_HOMOLOGUES (OrthoMCL clustering) 1.3 · CheckM 1.0.5 · Platanus assembler / Platanus_trim 1.2.4 / 1.0.7

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.

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.

Citations
300
Impact: very high
Foundation confidence
None of its references are in our reproducibility record yet — its foundation cannot be assessed.
Topics

No assessed neighbours yet — the network grows as more papers are assessed.

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.

GCA_000006785.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000006865.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000007785.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000014385.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000014445.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000014485.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000014545.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000159175.1 GCA in Discussion (http://purl.org/orb/Discussion)
no other assessed paper uses this yet
GCA_000159195.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_000191545.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_000193205.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000195575.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000219805.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000270185.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000283615.1 GCA in Methods (http://purl.org/orb/Methods)
no other assessed paper uses this yet
GCA_000319265.1 GCA in Discussion (http://purl.org/orb/Discussion)
no other assessed paper uses this yet
GCA_000409835.1 GCA in Discussion (http://purl.org/orb/Discussion)
no other assessed paper uses this yet
GCA_000410335.1 GCA in Discussion (http://purl.org/orb/Discussion)
no other assessed paper uses this yet
GCA_000463075.2 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_000469115.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001013375.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001063045.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001063065.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001064985.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001066235.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001068345.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001273585.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001314245.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001434215.1 GCA in Discussion (http://purl.org/orb/Discussion)
no other assessed paper uses this yet
GCA_001434555.1 GCA in Results (http://purl.org/orb/Results)
no other assessed paper uses this yet
GCA_001436985.1 GCA in Discussion (http://purl.org/orb/Discussion)
no other assessed paper uses this yet
GCA_001437115.1 GCA in Discussion (http://purl.org/orb/Discussion)
no other assessed paper uses this yet
PRJDB547 BioProject in Introduction (http://purl.org/orb/Introduction)
no other assessed paper uses this yet
PRJEB3060 BioProject in Introduction (http://purl.org/orb/Introduction)
no other assessed paper uses this yet
PRJNA222257 BioProject in Introduction (http://purl.org/orb/Introduction)
no other assessed paper uses this yet

What was reproduced

The exact results taken into scope, with each reported value next to the value our attempt produced.

Scope — PMID 27867804

Paper: Tanizawa Y, Fujisawa T, Kaminuma E, Nakamura Y, Arita M. "DFAST and DAGA: web-based integrated genome annotation tools and resources." Biosci Microbiota Food Health 2016;35(4):173–184. DOI 10.12938/bmfh.16-003 · PMCID PMC5107635.

Nature of paper: A resource/tools paper. It describes (a) the DFAST genome annotation pipeline (web service) and (b) the DAGA genome repository (1,421 Lactobacillus/Pediococcus genomes). The quantitative, reproducible computational core is the average nucleotide identity (ANI) analysis used to assess taxonomy and reveal genomic diversity.

Code artifact (per brief, P16 — third-party tool is fully valid): The paper itself names the tool: "The pyani script (https://github.com/widdowquinn/pyani) was used to calculate the ANI between two genomes, based on the method by Goris et al." So we reproduce by running pyani (the exact tool the authors used) on the exact genomes they used (read off Fig. 3 panels B/C/D, which label each leaf with its NCBI Assembly accession).

ANI method as described (verbatim, Methods → "Calculation of average nucleotide identity")

"one genome was cut into 1,020 nt fragments, which were searched against the other genome by using the BLASTN algorithm. ANI was calculated as the mean identity of top-hit BLASTN matches for all fragments with a sequence identity of ≥30% and an overall aligned region of ≥70% of the fragment length."

This is exactly pyani's ANIb method (BLAST+ blastn, 1020-nt fragments, Goris 2007 thresholds). Reproduction uses average_nucleotide_identity.py -m ANIb.

In scope (pipeline-derived, attempted) — all from Fig. 3

RESULT G — Lactobacillus gasseri two-subgroup separation (Fig. 3B).

  • Claim: two previously-unknown subgroups; ANI between subgroups ≈ 93%, within each subgroup > 98%.
  • Genomes (12, exact accessions from Fig. 3B): GCA_000175055.1, GCA_000283135.1, GCA_000177035.2, GCA_000177415.1, GCA_000155935.2, GCA_000439915.1, GCA_000014425.1 (ATCC 33323ᵀ), GCA_000176995.2, GCA_000814885.1, GCA_000406345.2, GCA_000143645.1, GCA_001063505.1.

RESULT J — Lactobacillus jensenii two-subgroup separation (Fig. 3C).

  • Claim: two subgroups; ANI between88%, within > 98%.
  • Genomes (14, exact accessions from Fig. 3C): GCA_000159335.1, GCA_001012735.1, GCA_001012745.1, GCA_001012675.1, GCA_001012655.1, GCA_000161895.2, GCA_000162435.1, GCA_001012665.1, GCA_001012685.1, GCA_000162335.1, GCA_001436455.1 (DSM 20557ᵀ), GCA_000466805.1, GCA_000155915.2, GCA_000175035.1.

RESULT D — L. delbrueckii six-subspecies type-strain ANI range (Fig. 3D + text).

  • Claim: ANI among the six subspecies type strains distributed in 97.2–98.4%.
  • Type-strain genomes (6):
    • subsp. delbrueckii: GCA_001908495.1 (DSM 20074ᵀ, assembly from type material)
    • subsp. bulgaricus: GCA_000056065.1 (ATCC 11842ᵀ)
    • subsp. lactis: GCA_000192165.1 (DSM 20072ᵀ)
    • subsp. indicus: GCA_001189855.1 (JCM 15610ᵀ)
    • subsp. sunkii: GCA_001190005.1 (JCM 17838ᵀ)
    • subsp. jakobsenii: GCA_001263315.1 (KACC 13439ᵀ)

Out of scope (not attempted) — and why

  • All-against-all ANI of 704 genomes (N = 247,456 comparisons; "all interspecific ANI < 95%; 198 of 7,616 intraspecific < 95%"). This is the hard ~20%: the exact 704-genome set is not enumerated in the paper (only summary counts), and 247k BLAST-based pairwise comparisons is a large compute. Skipped; the species-level subgroup analyses (G/J/D) test the same ANI machinery on enumerated genomes.
  • DFAST/Prokka annotation pipeline, CheckM quality grades, Platanus de-novo assembly of SRA reads. These are upstream/orthogonal; the headline ANI findings use the public NCBI Assembly genomes directly (Methods: genomes downloaded from NCBI Assembly DB), so we use those assemblies rather than re-assembling.
  • **Web service (dfast.nig.ac.jp), mislabe
Figures / tables: Fig 3BFig 3C
G_nsub
Reported
L. gasseri splits into 2 previously-unknown subgroups (Fig 3B)
Reproduced
2 subgroups (8 vs 3 genomes) by UPGMA on (1-ANI)
exact
G_between
Reported
ANI between L. gasseri subgroups ~93%
Reproduced
93.71% (min 93.40, max 94.31)
within tolerance
G_within
Reported
ANI within each L. gasseri subgroup >98%
Reproduced
mean 99.28% (min 97.99%)
within tolerance
J_nsub
Reported
L. jensenii splits into 2 previously-unknown subgroups (Fig 3C)
Reproduced
2 subgroups (7 vs 7 genomes) by UPGMA on (1-ANI)
exact
J_between
Reported
ANI between L. jensenii subgroups ~88%
Reproduced
88.06% (min 87.74, max 88.44)
exact
J_within
Reported
ANI within each L. jensenii subgroup >98%
Reproduced
mean 99.73% (min 99.43%)
exact
D_range
Reported
ANI among the six L. delbrueckii subspecies type strains distributed in 97.2-98.4%
Reproduced
14/15 type-strain pairs span 97.26-98.38% (matches); 1 outlier pair jakobsenii(KACC13439)<->delbrueckii(DSM20074)=99.97% makes the literal full 6x6 range 97.26-99.97%
partial

Assessments & scoring basis

Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.

🤖 AI curator · claude (ai-curator room) · v1.0 L1 89/100

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.

🟡1. Data identity
🟢2. Endpoint comparability
🟡3. Location of the main deviation
🟡4. Cause of the deviation
🟢5. Derivability / plausibility
🟢6. Severity of the deviation
🟢7. Core claim
🟡8. Severity of the miss (overall human judgment)
Scoring basis — itemised

Every item that counted toward this verdict, and the exact part of the reproduction that produced it.

Supporting (toward a concern)
Content-critical question only partially held
+2 pts
From: Q8 · Severity of the miss (overall human judgment) 🟡
Minor / cosmetic deviation
+1 pts
From: Q3 · Location of the main deviation 🟡
Minor / cosmetic deviation
+1 pts
From: Q4 · Cause of the deviation 🟡
Concordant (toward reproduced)
Code + data deposited & functional
-2 pts
From: Data & code availability Available & functional
Total score +2

The paper's reproducible computational core — the Fig.3 ANI analysis — reproduces cleanly: both novel findings (L. gasseri and L. jensenii each splitting into 2 subgroups, with ~93% and ~88% between-clade ANI and >98% within-clade ANI) match essentially 1:1, and 14/15 delbrueckii type-strain pairs land on the reported 97.2-98.4% range. The only real deviation — a 99.97% jakobsenii↔delbrueckii outlier — is an our-side assembly-provenance artifact (a likely post-paper near-duplicate assembly we selected), not an authors' defect. Remaining caveats (pyani version/ANIb-vs-blastall, one omitted gasseri genome, sample read off the figure) are technical/method-side and do not touch the conclusions. Overall: a strong reproduction with minor, fully explainable our-side deviations.

🤝
Reproduced automatically — and fairly

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.

Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.

🚩 Report an error in this record

Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.

Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.

Reproduction footprint

claude-opus-4-8

Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.

251.4 k
tokens (I/O) · 23.5 M incl. cache
52 min
runtime · 11.12 CPU-h
5.7 GB
peak RAM
1
HPC jobs
hummel
machine