genomepy: genes and genomes at your fingertips.
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. genomepy is a software-tool application note (no experimental data deposit; the Zenodo accession is the source archive). Reproduction = installing the pinned tool (genomepy 0.16.1, bioconda) inside a SLURM job on a «our HPC» compute node and verifying its documented behaviours 1:1 against live provider data. All five in-scope behavioural claims reproduced: C1 (4 remote providers Ensembl/UCSC/NCBI/GENCODE — exact), C2 (all 6 aligner-index plugins present; bowtie2+bwa indexes actually built during install — exact), C3 (install emits exactly the documented file set FASTA/.fai/.sizes/gaps-BED/GTF/BED/README for R64-1-1 — exact), C4 (provenance README with provider/accession/URLs/version/date + GTF & BED12 annotation — within-tol: this version's README has no explicit checksum field), C5 (search returns assemblies from all 4 providers; live counts Ensembl 1118 / UCSC 220 / NCBI 3.65M / GENCODE 4 — exact, scale claim met and exceeded). Genome profile (17 sequences, 12,157,105 bp) matches the published S. cerevisiae R64 assembly. Key reproducibility gotcha for 2023-era genomepy under a modern toolchain: pin setuptools<80 + python<=3.10 (dep norns imports pkg_resources, removed in setuptools>=81) and redirect XDG_CACHE_HOME/XDG_CONFIG_HOME off a read-only HOME on HPC compute nodes. NOT attempted: C6 (Suppl. Table S2 large-genome timing/disk benchmark) and C7 (full Suppl. Table S1 feature matrix) — performance/documentation context rather than core pipeline outputs. Grades are provisional; a human auditor signs off against the exact paper wording.
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.
✎ 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
- 2026-06-30
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-30no 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: sonnet- ★ genomepy can search, download, and preprocess reference genome and gene annotation data from NCBI, Ensembl, UCSC, and GENCODE resource
- ★ Manually supplying genomic resources for bioinformatic workflows is tedious and error-prone, motivating an automated tool finding
- ★ genomepy inspects available gene annotations via an 'annotation' function to enable informed selection before download method
- ★ The 'install' function downloads and processes a selected genome assembly (masking level, alternate sequences, contig filtering) and matches gene annotation contig names to the genome FASTA method
- ★ genomepy supports plugins for postprocessing, including blacklist download (Kundaje lab) and generation of aligner indexes (Bowtie2, BWA, GMAP, Minimap2, STAR, HISAT2) method
- ★ genomepy generates a README documenting time, source files, and processing steps for data provenance and reproducibility resource
- genomepy has been incorporated into other packages such as pybedtools and CellOracle finding
- genomepy is freely available under the MIT license via GitHub, pip, and Bioconda resource
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 announcement (Applications Note) describing genomepy, a command-line and Python tool for searching, downloading, and preprocessing reference genome and gene annotation data. The paper does not report an experimental study, hypothesis test, or quantitative comparison between conditions or groups; it is a methods/software description rather than a statistical analysis.
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The paper states genomepy 'work[s] efficiently with large genomic data as shown in Supplementary Table S2' without quantitative benchmark statistics in the main text.↳ Could also: Reporting runtime/memory benchmarks with summary statistics (e.g., mean ± SD or median with IQR across repeated runs) and comparison to existing tools (iGenomes, refGenie, Go Get Data) — Quantified, repeated-measure benchmarking with a dispersion estimate would let readers gauge the consistency and reproducibility of performance claims, which is a common practice in software/tool papers reporting efficiency.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36882164 (genomepy: genes and genomes at your fingertips)
Paper: Frölich S, van der Sande M, Schäfers T, van Heeringen SJ. genomepy: genes
and genomes at your fingertips. Bioinformatics 39(3):btad119, 2023.
Type: Application Note describing a software TOOL (not a data-analysis study).
Code: https://github.com/vanheeringen-lab/genomepy (authors' own — P16 N/A).
Archive: Zenodo concept DOI 10.5281/zenodo.1010458 → record 8038749 =
vanheeringen-lab/genomepy-0.16.1.zip (845.7 kB, software source archive, 2023-06-14).
There is no separate data deposit — genomepy is a downloader/processor that queries
live provider databases (Ensembl, UCSC, NCBI, GENCODE) at runtime.
Nature of "results" in this paper
A tool note. Its reproducible, pipeline-derived assertions are the documented
behaviours of the tool itself: which providers/aligners it supports, what files
install produces, and an efficiency benchmark (Suppl. Table S2). "Reproduction"
here = install the pinned tool on «our HPC» and verify these behaviours 1:1 by running it.
IN SCOPE (reproducible by running genomepy on «our HPC»)
| id | claim (paper) | how to reproduce | pipeline |
|---|---|---|---|
| C1 | Supports 4 remote providers: Ensembl, UCSC, NCBI, GENCODE | genomepy providers |
genomepy CLI |
| C2 | Builds aligner indexes for Bowtie2, BWA, GMAP, HISAT2, Minimap2, STAR | genomepy plugin list / source |
genomepy plugins |
| C3 | install produces genome FASTA + .fai + .sizes + gaps BED + annotation (GTF & BED) + README.txt provenance |
run genomepy install on a real assembly, list outputs |
genomepy install |
| C4 | Annotation handled in GFF3/GTF/BED12; provenance README records provider, accession, checksums, options | inspect README.txt + annotation files | genomepy annotation |
| C5 | search returns available assemblies from each provider (works at provider scale, tens of thousands of genomes) |
genomepy search per provider; count provider genome list |
genomepy search |
| C6 | Suppl. Table S2: works efficiently with large genomic data (timing/disk for a large genome install) | time genomepy install of a large assembly on a compute node (SLURM) |
genomepy install (heavy) |
| C7 | Genomepy column of Suppl. Table S1 feature matrix (the features genomepy itself provides) | verify each claimed genomepy feature exists | genomepy CLI/API |
OUT OF SCOPE (not attempted)
- Feature claims about other tools in Suppl. Table S1 (refgenie, iGenomes, Galaxy data managers, ...): comparison context, not a genomepy result to reproduce.
- Any provider-side numbers that change daily (exact live assembly counts): treated as order-of-magnitude / "uncheckable-exact" not as a fixed value to match.
- No wet-lab / manual / external results exist (pure software note).
Pinning
- Tool version: genomepy 0.16.1 (the Zenodo-archived, paper-associated release; June 2023, nearest to the March-2023 publication). Install via bioconda.
- Commit: tag
0.16.1=1be338be25ddb9c6db4d13d5ca202677bec9b159. - Note: paper text install hint is
genomepy>=0.16; README examples used for C3–C5.
Datasets to profile (DATASET PROFILING pass)
- Zenodo 8038749 (concept 10.5281/zenodo.1010458) — genomepy source archive (software).
- The genome actually downloaded during the functional
install(e.g. a real assembly from UCSC/Ensembl) — profile what genomepy fetched & produced.
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 software-tool reproduction. genomepy is an application note; installing the pinned 0.16.1 release on a «our HPC» compute node reproduced all five in-scope behavioural claims — 4 remote providers (C1), 6 aligner-index plugins with bowtie2+bwa built end-to-end (C2), the documented R64-1-1 install file set (C3), and provider search at scale (C5, even exceeding the 'tens of thousands' figure). The sole deviation is C4 (within-tol): the 0.16.1 README captures full provenance metadata but has no explicit checksum field, a minor version-specific documentation point whose paper wording could not be verified. No authors'-side or derivability concern — every value follows from running open code on open data.
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.
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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.