getSequenceInfo: a suite of tools allowing to get genome sequence information from public repositories.
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
REPRODUCED. getSequenceInfo (tool paper, P16) reproduced by running the authors' own Perl suite on the paper's example accessions + reference genomes on «our HPC» front1. R1 SRArunInfo: 3/3 reported base counts reproduced to the digit + taxid/species/platform exact (the tool's legacy NCBI runinfo endpoint is deprecated -> swapped for its official efetch-runinfo replacement, tool logic unchanged; the underlying values match ENA exactly). R2 NucleScore: 7/7 per-species scores on reference genomes fall inside the published Fig.3 ranges (deterministic metric). R3 dataset: shipped Additional file 2 holds exactly 2518/1835/622/61 as reported, and the live harvest function works end-to-end (downloads genomes + builds summary.xls incl. NucleScore); current Complete-Genome counts are larger (5118/3442/65), consistent with public-DB growth since 2021. Tool -fastq function downloaded SRR7693912 with byte-exact md5 match to ENA. NOT attempted (out of scope): the AMR/plasmid/virulence + MLST sheets in Additional file 2 (produced by external typing tools, not getSequenceInfo), the Tk GUI, and Galaxy/Singularity packaging.
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 99assessed: 2026-06-21 ⛓ 2d0a699c6571
✎ 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
- 2026-06-21
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-21no 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: sonnetThe paper does not test a scientific hypothesis but addresses whether a unified, accessible software tool (getSequenceInfo/gSeqI) can retrieve genome sequences and associated metadata from public repositories (GenBank, RefSeq, ENA) in a simple programmatic or GUI-based way, and whether a novel nucleotide-composition-based metric (NucleScore) can help delineate/characterize genome assemblies without a reference genome.
- ★ getSequenceInfo (gSeqI) allows programmatic (CLI) or GUI-based retrieval of sequence data and metadata from GenBank, RefSeq, and ENA across Linux, MacOS, and Windows. resource
- ★ The empirically designed NucleScore, based on nucleotide variance, GC content, AT/GC ratio, and genome length, can distinguish species using nucleotide information alone without needing a reference genome. mechanism
- ★ getSequenceInfo differs from existing download tools by linking queries to metadata (country, host, release date) and separating chromosomes from plasmids/other genetic elements. finding
- Supplementary tools (nucleScore.pl, countDifferences.pl, SRArunInfo.pl, removeChar.pl) extend sequence analysis functionality beyond the main gSeqI tool. resource
- getSequenceInfo and supplementary tools are partly accessible via the Galaxy KaruBioNet platform for users uncomfortable with CLI/GUI. resource
- catchSequenceInfo (Galaxy) provides resistance, virulence, plasmid, and MLST information from complete genome assemblies. resource
- ★ NucleScore has limitations: closely related species such as Escherichia coli and Salmonella enterica show overlapping/close NucleScore values, reducing discriminatory power. finding
- The main tool is available as a Singularity container, with a Docker container planned. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| NucleScore calculation (nucleotide composition metrics) | Genome assemblies of 7 bacterial species (S. aureus, E. coli, A. baumannii, S. enterica, B. cereus, B. subtilis, K. pneumoniae) | none | NucleScore value (min/max) per species | nucleScore.pl (getSequenceInfo suite) |
| Resistance/plasmid/virulence gene detection | Complete bacterial genome assemblies (E. coli n=1835, K. pneumoniae n=622, E. cloacae n=61), mainly from RefSeq | none | Presence/absence of resistance, plasmid, and virulence genes | ABRicate with ResFinder, PlasmidFinder, and VFDB databases |
| MLST typing | Same complete bacterial genome assemblies (E. coli, K. pneumoniae, E. cloacae) | none | Sequence type | mlst tool (tseemann) |
| Sequence and metadata retrieval from NCBI | Coronavirus (viral kingdom) genome assemblies | none | FASTA/GenBank files with associated metadata (country, release date, host) | getSequenceInfo.pl (NCBI GenBank/RefSeq) |
| Sequence and metadata retrieval from NCBI | Escherichia coli complete genome assemblies and associated plasmids (RefSeq) | none | Downloaded plasmid/chromosome FASTA files | getSequenceInfo.pl |
| FASTQ read retrieval | Mycobacterium bovis genomes (Brazilian isolates) | none | Downloaded compressed FASTQ files | getSequenceInfo.pl (ENA run accessions) |
| Multi-FASTA alignment comparison | User-provided multi-FASTA alignment file | none | Pairwise sequence differences (bp) and percentage identity matrices | countDifferences.pl |
| SRA run accession information retrieval | SRA run accessions (e.g. SRR7693912) | none | Run summary information (XML/CSV outputs) | SRArunInfo.pl |
- – NucleScore values distinguish Staphylococcus aureus and Klebsiella pneumoniae with clearly non-overlapping ranges. S. aureus 4.655–4.768 vs K. pneumoniae 0.140–0.195
- – NucleScore ranges for Escherichia coli and Salmonella enterica are very close, limiting species discrimination. E. coli 0.978–1.125 vs S. enterica 0.838–0.895
- – A dataset of complete genome assemblies across three species was built using getSeqI and catchSequenceInfo. E. coli n=1835, K. pneumoniae n=622, E. cloacae n=61
- – Resistance and plasmid gene screening was performed using a defined coverage threshold. 90.00% coverage
- – Virulence gene screening was performed using a defined coverage threshold. 80.00% coverage
- – Example query retrieved coronavirus genome assemblies available from a given release date. 50 genomes
- – Galaxy instance downloads are capped due to limited data storage capacity. max 50 assemblies
- other 4.655–4.768 (NucleScore range for Staphylococcus aureus (min–max))
- other 0.978–1.125 (NucleScore range for Escherichia coli (min–max))
- other 3.146–3.256 (NucleScore range for Acinetobacter baumannii (min–max))
- other 0.838–0.895 (NucleScore range for Salmonella enterica (min–max))
- other 3.66–3.764 (NucleScore range for Bacillus cereus (min–max))
- other 1.858–2.369 (NucleScore range for Bacillus subtilis (min–max))
- other 0.140–0.195 (NucleScore range for Klebsiella pneumoniae (min–max))
- count 1835, 622, 61 (Number of genome assemblies analyzed for E. coli, K. pneumoniae, and E. cloacae respectively)
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 software/tool paper (describing the getSequenceInfo suite) rather than a hypothesis-driven experimental study. The only quantitative comparison presented is a descriptive one: a custom composite metric ('NucleScore', derived from nucleotide variance, GC content, AT/GC ratio, and genome length) is computed for 50 genome assemblies spanning 7 bacterial species, and results are summarized as per-species min-max ranges (Fig. 3) to illustrate potential species discrimination. No formal inferential statistical tests, p-values, or multiplicity corrections are reported anywhere in the text.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Descriptive summary (min-max range) of a computed composite score; no formal inferential/hypothesis test is described | Comparison of NucleScore values across 7 bacterial species (Fig. 3) | 50 genome assemblies across 7 species (per-species n not individually stated) | not stated |
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Species differences in NucleScore were compared only by reporting min-max ranges per species (Fig. 3), without a formal statistical test.↳ Could also: A nonparametric test such as Kruskal-Wallis (with post-hoc Dunn's test) or, if assumptions permit, one-way ANOVA with Tukey HSD — This would provide a formal quantification (e.g., p-values, effect sizes) of how distinguishable the species' NucleScore distributions are, complementing the visual/range-based comparison.
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Only minimum and maximum NucleScore values are reported for each species rather than measures of central tendency and spread.↳ Could also: Reporting mean or median with SD or IQR, and/or displaying the full distribution (e.g., boxplot or violin plot) — Central tendency and spread measures convey how typical values are distributed within a species and how much overlap exists between species, which min-max alone (sensitive to outliers) does not capture.
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The paper notes qualitatively that E. coli and Salmonella enterica NucleScore ranges are close, presented as a visual/numeric observation.↳ Could also: A supervised classification approach (e.g., linear discriminant analysis or a simple classifier using GC content, AT/GC ratio, and length as features) evaluated via cross-validation — This would provide a quantitative separability metric (e.g., classification accuracy, confusion matrix) between closely-scoring species, complementing the descriptive range comparison.
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The NucleScore's potential use for assessing genome assembly quality/completeness is proposed conceptually but not tested against an established quality benchmark.↳ Could also: Correlating NucleScore with established assembly-quality metrics (e.g., BUSCO completeness, N50) using a correlation coefficient (e.g., Spearman's rho) — This would offer an external validation point for the proposed score's relationship to recognized quality/completeness measures.
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The 50-genome, 7-species dataset used for the NucleScore illustration does not state per-species sample sizes or how species/genomes were selected.↳ Could also: A pre-specified, balanced sampling scheme (equal n per species) with per-species n explicitly reported — Explicit, balanced per-group n makes it easier for readers to judge how stable the reported min-max ranges are likely to be for each species.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35804320 (getSequenceInfo)
Paper: Moco et al. 2022, BMC Bioinformatics. "getSequenceInfo: a suite of tools allowing to get genome sequence information from public repositories." (gSeqI v1.0) Repo: https://github.com/karubiotools/getSequenceInfo (GPLv3, Perl; latest commit 162b42f 2025-03-29). This is a tool paper (P16: a tool applied to public data; equally valid to reproduce by running the tool on the paper's own data/examples).
Pipeline-derived results IN SCOPE (reproduce by running the tool)
- R1 — SRArunInfo.pl table (Additional file 1). Run
SRArunInfo.plon the paper's example SRA runs and compare the retrieved per-run metadata (Bases bp, TaxonomyID, species, country, platform/model) to the shipped output table. Accessions: SRR7693877, SRR9850824, SRR9850830 (Additional file 1) + the RU's named accession SRR7693912 (Example 5 fastq demo). Pipeline: SRArunInfo.pl -> NCBI Trace runinfo CSV + SRA FullXml. Stable metadata -> directly reproducible. - R2 — NucleScore (Fig. 3).
nucleScore.pl/nucleScore.pycompute NucleScore = log2((var(A,T,G,C,N %) * GC% * (AT/GC ratio)^3) / sqrt(length)). Deterministic per FASTA. Fig. 3 gives per-species ranges; reproduce by computing NucleScore on reference genomes of the 7 species and checking each falls in the published range. Pipeline: nucleScore.pl on a genome FASTA. - R3 — "2,518 complete genome assemblies" dataset (Additional file 2, getSequenceInfo sheet) + per-species N. getSequenceInfo.pl harvested Complete Genome assemblies of E. coli (n=1835), K. pneumoniae (n=622), E. cloacae (n=61), total 2518. Reproduce the TOOL FUNCTION via a live download run; verify the reported N against the shipped deposit (consistency). NOTE: a live RefSeq/GenBank re-harvest in 2026 will NOT match 2021 counts (the public DB has grown), so the exact-N reproduction is a deposit-consistency check, not a live-count match.
OUT OF SCOPE (external tools / not getSequenceInfo)
- catchSequenceInfo sheet + the three "ST (MLST)" sheets in Additional file 2: resistance/plasmid/virulence (AMRFinder/abricate-class) and MLST ST assignments are produced by EXTERNAL typing tools, not getSequenceInfo. Counts noted for audit but not reproduced here (different pipeline; the paper's own contribution is the getSequenceInfo retrieval suite).
- GUI (Perl/Tk), Galaxy wrapper, Singularity/Docker packaging — installation/UX, not a numeric pipeline result.
Data
- SRA: SRR7693912 (+ siblings SRR7693877, SRR9850824, SRR9850830) — Illumina WGS, Mycobacterium tuberculosis var. bovis, Brazil (Univ. Sao Paulo).
- Shipped example outputs: datasets/Additional_file_1*.xls, Additional_file_2*.xls (the authors' own tool outputs = the ground-truth claims to compare against).
- Live RefSeq/GenBank assemblies (for R2/R3), fetched on «our HPC» front1 to «infra».
Compute
All runs on «our HPC» front1 (light: metadata fetch + Perl/BioPerl on small FASTAs); data + repo clone on «infra» work dir. No heavy SLURM compute needed.
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.
Clean 1:1 reproduction of a tool paper. Running the authors' own getSequenceInfo suite on the paper's example accessions reproduced all three SRArunInfo base counts to the digit (1086290059/380025436/426439836), taxid 1765 + species + platform exactly, 7/7 NucleScores inside the published Fig.3 ranges, and the shipped Additional file 2 dataset counts (2518/1835/622/61) exactly; the -fastq download matched ENA byte-exact. The only deviations are on the infrastructure side, not the authors': one deprecated NCBI download URL swapped for its official replacement (parsing unchanged, numbers verified against ENA) and a blank country metadata field from a dead FullXml endpoint. Live 2026 RefSeq counts are larger (5118/3442/65) purely from monotonic public-DB growth since the 2021 snapshot, so they were correctly graded against the deposit. No fabrication indicators.
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.