pysradb: A Python package to query next-generation sequencing metadata and data from NCBI Sequence Read Archive.
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 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
- 🟡A deviation arose in the data or preprocessing
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 -> 1:1 reproduction. pysradb is a software-tool paper; its reported results are the Use-cases command outputs. Re-ran the exact pysradb commands on the paper's exact public accessions on a «our HPC» compute node («job», pysradb 2.5.1 / Python 3.10, SRAweb backend). 4 of 5 claims EXACT incl. the BRIEF headline GSE24355->SRP003870 (C1) and the full SRP010679 metadata table (C4, all 12 experiments/samples/runs). C5 (SRP000941 assay-type counts) is partial: 4 of 5 buckets identical to the unit (999/768/353/28), only the catch-all OTHER drifted 121->59 -- expected because SRP000941 (NIH Roadmap Epigenomics) is an actively re-curated study and the paper's counts came from a frozen 2019 SRAmetadb.sqlite snapshot while we used the live SRAweb backend. No fabrication signals: every reported value is regenerable from the named public accessions via the published tool. NOT attempted (out of scope per 80/20): pysradb download (fetches GBs of FASTQ/SRA, no checkable reported value), pysradb search free-text (unbounded/time-dependent), and downloading SRAmetadb.sqlite (upstream snapshot no longer hosted; not needed for the accession mappings).
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 90assessed: 2026-06-14 ⛓ 81145ae2c9eb
✎ 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
- 2026-06-14
- 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: opusProgrammatic access to NCBI Sequence Read Archive (SRA) metadata and data is limited and often requires familiarity with a programming language; the paper introduces pysradb, a Python package providing a command-line interface to query and download SRA metadata and data using the curated SRAdb SQLite database.
- ★ pysradb provides a simple, user-friendly command-line interface for querying metadata and downloading datasets from SRA without requiring knowledge of a programming language. resource
- ★ pysradb builds upon the principles of the SRAdb project and utilizes its frequently updated SRAmetadb.sqlite SQLite database for metadata queries. method
- ★ pysradb offers a chain of sub-commands to retrieve metadata, convert between accession types (e.g., GSE-to-SRP, GSE-to-GSM, GSM-to-SRR), and download datasets. method
- ★ pysradb enables seamless downloads from SRA, organizing data following the NCBI hierarchy (SRP => SRX => SRR) and supporting Unix pipe-based inputs for subsetting (e.g., by assay). method
- pysradb is written in Python 3, is open-source under the BSD 3-Clause License, runs on Linux and Mac, and is installable via PyPI and bioconda. resource
- pysradb uses pandas for data frame operations and tqdm to display progress for long-running download tasks. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| metadata query (search sub-command) | SRA SRAmetadb.sqlite database (schema v1.0, timestamp 2019-01-25) | none | study/experiment/sample/run accessions matching keyword (e.g., 'ribosome profiling') | SRAmetadb.sqlite |
| metadata retrieval (metadata sub-command with --desc/--expand) | SRA project SRP010679 (PC3 human prostate cancer cells, Ribo-seq/RNA-seq) | drug treatment (vehicle, rapamycin, PP242) | experiment/sample/run accessions and sample attributes (cell line, sample type, treatment) | SRAmetadb.sqlite |
| assay-type tabulation (metadata --assay) | SRA project SRP000941 | none | counts of assay types (Bisulfite-Seq, ChIP-Seq, OTHER, RNA-Seq, WGS) | SRAmetadb.sqlite |
| accession conversion (gse-to-srp) | GEO studies GSE24355, GSE25842, GSE100007 | none | corresponding SRP study (and experiment/sample) accessions | SRAmetadb.sqlite |
| accession conversion (gse-to-gsm with --desc/--expand) | GEO study GSE41637 (mouse tissues, strain DBA/2J) | none | GSM experiment aliases and sample attributes (source, strain, tissue) | SRAmetadb.sqlite |
| accession conversion (gsm-to-srr) | GEO experiments GSM1020640, GSM1020646 | none | corresponding SRA run (SRR) accessions | SRAmetadb.sqlite |
| data download (download sub-command) | SRA projects SRP000941, SRP010679 | none | downloaded raw sequencing data organized by SRP/SRX/SRR | NCBI SRA |
- – Searching 'ribosome profiling' returned relevant SRA projects (e.g., DRP003075, ERP013565) with associated study/experiment/sample/run accessions.
- – metadata for SRP010679 resolved experiment, sample, and run accessions and expanded sample attributes into columns (cell line PC3; treatments vehicle/rapamycin/PP242).
- – Assay-type breakdown for SRP000941 across runs. 999 Bisulfite-Seq, 768 ChIP-Seq, 121 OTHER, 353 RNA-Seq, 28 WGS
- – gse-to-srp converted GSE24355 to SRP003870 and GSE25842 to SRP005378.
- – gsm-to-srr converted GSM1020640 to SRR594393 and GSM1020646 to SRR594399.
- – download with a piped, grep-filtered metadata stream downloads only RNA-Seq samples from a project.
- count 999 (Bisulfite-Seq runs in SRP000941)
- count 768 (ChIP-Seq runs in SRP000941)
- count 353 (RNA-Seq runs in SRP000941)
- count 121 (OTHER assay runs in SRP000941)
- count 28 (WGS runs in SRP000941)
Statistical methods review
Model: opusA 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 article describing pysradb, a Python command-line package for querying and downloading next-generation sequencing metadata and data from the NCBI Sequence Read Archive. The work is methodological/engineering in nature: it presents implementation details, continuous-integration testing across Python versions and operating systems, and demonstrative use cases on public accessions. No experimental design, hypothesis testing, or inferential statistical analysis of data is reported.
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The paper validates the tool primarily through continuous-integration unit tests and demonstrative use cases on selected public accessions.↳ Could also: A quantitative benchmark comparing query/download runtime or completeness against existing tools (e.g., SRAdb in R or Entrez efetch), reported with summary statistics across repeated runs, could also be presented. — Such a benchmark would add a descriptive, reproducible performance characterization and let readers compare tools on common metrics; it is a common complement for software tool papers.
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Demonstration outputs are presented as single illustrative command runs (one table per example).↳ Could also: Repeating timed operations and summarizing central tendency and spread (e.g., median with IQR or mean with a 95% CI) would also be an option for any timing or coverage claims. — Reporting a measure of spread alongside a point estimate conveys run-to-run variability and is often preferred when any performance figures are quoted.
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Correctness of accession conversions is shown via example mappings.↳ Could also: A systematic accuracy check against a gold-standard set of known GSE↔SRP↔SRR mappings, reported as a proportion (with a binomial confidence interval), could also be used. — A proportion-with-CI summary would give a descriptive, quantitative sense of mapping reliability across many records rather than selected examples.
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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GEO-to-SRA accession conversion maps GSE24355 to SRP003870 and GSE25842 to SRP005378other geo 2019×1papers★ This paper is the founder (earliest)
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GSM-to-SRR accession conversion maps GSM1020640 to SRR594393 and GSM1020646 to SRR594399other geo 2019×1papers★ This paper is the founder (earliest)
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SRA metadata retrieval for SRP010679 resolves run accessions and expands sample attributes for PC3 human prostate cancer cells treated with rapamycin or PP242other pc3 2019×1papers★ This paper is the founder (earliest)
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SRA keyword search for 'ribosome profiling' returns relevant project accessions including DRP003075 and ERP013565 with associated study/experiment/sample/run accessionsother sra 2019×1papers★ This paper is the founder (earliest)
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pysradb download with grep-filtered metadata stream selectively retrieves only RNA-seq samples from a multi-assay SRA projectother sra 2019×1papers★ This paper is the founder (earliest)
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.
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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-31114675 (pysradb)
Paper: Choudhary S. pysradb: A Python package to query next-generation sequencing metadata and data from NCBI Sequence Read Archive. F1000Research 2019. DOI 10.12688/f1000research.18676.1 · PMID 31114675 · PMCID PMC6505635.
Nature of the paper. This is a software tool paper. There is no wet-lab
experiment and no statistical figure to redo. The paper's "results" are the
worked command-line examples in the Use cases / Methods section: each shows
a pysradb subcommand and the metadata it returns (accession mappings, metadata
tables, assay-type counts). These outputs ARE the pipeline-derived results.
The "pipeline" here = pysradb itself (the published tool, https://github.com/saketkc/pysradb), applied to the public NCBI SRA / GEO metadata. Per BRIEF rule P16, running the tool on the paper's own example accessions is a fully valid 1:1 reproduction.
In scope (deterministic metadata-mapping outputs — reproduce these)
These map public accessions to public accessions; they are stable facts in GEO/SRA and must reproduce exactly regardless of metadata-backend version.
| id | command (from paper) | reported output |
|---|---|---|
| C1 | pysradb gse-to-srp GSE24355 GSE25842 |
GSE24355→SRP003870 ; GSE25842→SRP005378 |
| C2 | pysradb gse-to-gsm GSE41637 |
GSM1020640…GSM1020648 (9 GSMs) |
| C3 | pysradb gsm-to-srr GSM1020640 GSM1020646 |
GSM1020640→SRR594393 ; GSM1020646→SRR594399 |
| C4 | pysradb metadata SRP010679 |
experiments SRX118285–SRX118296 (12); samples SRS290854–SRS290865; runs SRR403882–SRR403893 |
GSE24355 is the BRIEF's headline dataset → its SRP mapping (C1) is the primary claim.
In scope but expected to DRIFT (the "hard 20%")
| id | command | reported output | note |
|---|---|---|---|
| C5 | pysradb metadata SRP000941 --assay | … | uniq -c |
999 Bisulfite-Seq, 768 ChIP-Seq, 121 OTHER, 353 RNA-Seq, 28 WGS | SRP000941 = NIH Roadmap Epigenomics, an actively growing project. The paper's counts came from the SRAmetadb.sqlite snapshot dated 2019-01-25; live SRAweb counts will be ≥ those. Attempt, but a mismatch here is expected and is NOT evidence of fabrication. |
Out of scope (not attempted, with reason)
pysradb downloadexamples — they fetch raw FASTQ/SRA (GBs); the result is bytes on disk, nothing the paper reports a checkable value for. Skipped per 80/20.pysradb search '"ribosome profiling"'— free-text search over the whole live SRA; output is unbounded and time-dependent, no fixed reported value. Skipped.pysradb srametadb(download SRAmetadb.sqlite) — the upstream snapshot is no longer hosted/updated; not required for the in-scope mappings (SRAweb backend gives the same accession mappings). Skipped.
Backend note (faithfulness)
The 2019 paper used the local SRAmetadb.sqlite snapshot. Current pysradb defaults to the live SRAweb (NCBI Entrez) backend. The accession mappings (C1–C4) are immutable identifiers and reproduce identically across backends; only aggregate counts over growing projects (C5) can drift. We reproduce with the tool's current default backend and flag this explicitly.
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.
Software-tool paper: re-running pysradb 2.5.1 on the paper's exact public accessions reproduces 4/5 claims exactly, including all immutable GSE/GSM/SRP/SRX/SRS/SRR mappings. The single deviation is C5's --assay OTHER bucket (121->59), confined to one catch-all category while the other four match to the unit; this sits on the data/input version side (frozen 2019 SRAmetadb snapshot vs live SRAweb on an actively re-curated study), not on the authors' side or the reproduction logic. Severity is low and the tool's central claim holds; no fabrication concern.
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.
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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.