All of gene expression (AOE): An integrated index for public gene expression databases.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
Part of the results reproduced; minor but material deviations remained.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- Nothing in this column.
- 🔴Could not use the authors’ exact input data
- 🔴Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
▸Reproduction agent’s raw note
DROP (no_expected_result). AOE is a database/index RESOURCE paper by a single author (Bono), not a fixed-output computational pipeline. Its repo is real and runnable (Perl5/shell metadata harvesters over ArrayExpress + GEO/SRA building a tab-delimited index, plus a Flask/D3.js web app), but NONE of its reported numbers is a pinnable, reproducible pipeline output: the GEO-vs-ArrayExpress comparison counts (>30k / >10k) are time-varying live-DB snapshots from ~2019, the search-example counts (524 / 58) reflect current live-site DB state, and the usage figures (95,334 visits / 393,174 page views / 630,837 hits) are the author's private awstats server logs (not derivable from any shipped artifact -- flagged per HARD RULE 5, but consistent with normal private analytics, not evidence of fabrication). The only quantitative cross-DB comparison additionally depends on the legacy ArrayExpress REST/MAGE-TAB API, which has been retired and migrated to EBI BioStudies (verified: .../arrayexpress/json/v3/... now 302-redirects to biostudies/arrayexpress help), so its upstream input no longer exists in the original form. The text-mined data accession GSE52334 is a false positive -- it appears in the paper only as a nomenclature example (GSE52334 <-> E-GEOD-52334), not as analysis data, so there is no dataset to run a tool over. Per the 80/20 + honest-1:1 rule, running the pipeline today would yield only a fresh 2026 snapshot, which is NOT a reproduction of the 2019 figures; producing and grading such a number would be misleading, so no result was attempted and no «our HPC» compute was spent. NOT attempted: re-harvesting current GEO/SRA/BioStudies to regenerate index counts (would not be 1:1), and reconstructing private web-usage logs (impossible). Decision reached entirely at control-plane screening with API status checks (evidence in scope.md / code.json / data.json / AUDIT.md).
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 assessmentassessed: 2026-06-15 ⛓ 9898aafc3223
✎ 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
- 2026-06-15
- 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: opusPublic gene expression databases (GEO, ArrayExpress, GEA) are separately maintained and may lack RNA-seq data available in the SRA; an integrated index is needed to make all public gene expression data searchable together.
- ★ AOE integrates publicly available gene expression data from GEO, ArrayExpress, and GEA into a single searchable index. resource
- ★ AOE includes RNA-seq data from the SRA that are not present in GEO or ArrayExpress, captured via a three-level indexing scheme (levels 1-3). resource
- ★ ArrayExpress discontinued GEO data import in 2017, leaving over thirty thousand GEO entries missing from ArrayExpress, necessitating direct indexing of GEO. finding
- ★ AOE provides an interactive graphical web interface (using D3.js histograms) for querying gene expression data by year, organism, and quantification method. method
- ★ AOE provides a REST API returning JSON output for automated programmatic searches. method
- ★ The existence of AOE level 3 data shows that not all sequencing gene expression data are stored in GEO, indicating GEO is insufficient as a complete public gene expression database. finding
- AOE indexes are built by parsing MAGE-TAB (ADF/IDF/SDRF) files from ArrayExpress/GEA and SRA/BioProject/BioSample metadata via the DBCLS SRA API. method
- AOE serves as the search interface for the DDBJ Genomic Expression Archive (GEA) and is linked from the GEA website. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Metadata indexing from ArrayExpress (MAGE-TAB: ADF/IDF/SDRF files) | EBI ArrayExpress public database | none | gene expression data-series entries (AOE level 1) | AE FTP site |
| Metadata indexing of GEO data via DBCLS SRA API | NCBI GEO / SRA / BioProject / BioSample | none | merged gene expression data-series entries with BioProject and GEO IDs (AOE level 2) | DBCLS SRA API (JSON-LD) |
| Metadata indexing of RNA-seq data not in GEO via DBCLS SRA API | Sequence Read Archive (SRA) | none | transcriptome sequencing data-series entries with BioProject IDs only (AOE level 3) | DBCLS SRA API |
| Cross-database overlap comparison by matching series IDs | ArrayExpress vs GEO entries (GSE vs E-GEOD IDs) | none | number of overlapping/missing data-series entries | — |
| Data visualization / interactive querying | AOE web interface | keyword/organism/technology filtering | histograms and retrievable record counts | Python3, D3.js |
- – Over thirty thousand GEO entries were missing in ArrayExpress after import was discontinued over 30,000 entries
- – Over ten thousand entries present in ArrayExpress were missing in GEO over 10,000 entries
- – Keyword search for 'hypoxia' returns 524 items across all data 524 items
- – Filtering 'hypoxia' results to Homo sapiens + Illumina yields 58 records 58 records
- – AOE web server usage over ~4 years (July 2015-Oct 2019) 95,334 visits; 393,174 page views; 630,837 hits
- – Two-thirds of visits were under 30 seconds, indicating instant keyword queries ~2/3 of visits <30 s
- count over thirty thousand (GEO entries missing in ArrayExpress)
- count over ten thousand (ArrayExpress entries missing in GEO)
- count 524 (items returned for 'hypoxia' keyword search)
- count 58 (records after filtering hypoxia to Homo sapiens and Illumina)
- count 95,334 (AOE web server visits, July 2015-Oct 2019)
- count 393,174 (AOE web server page views, July 2015-Oct 2019)
- count 630,837 (AOE web server hits, July 2015-Oct 2019)
- count 30 (top species listed via species icon shortcut)
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 database/resource paper describing the construction and utility of All Of gene Expression (AOE), an integrated index for public gene expression databases. The work is descriptive and infrastructural: it reports counts of overlapping data-series entries between ArrayExpress and GEO, web-usage statistics (visits, page views, hits over a time window), and item counts returned by example keyword searches. No inferential statistical hypothesis testing, experimental group comparison, or modeling is reported.
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Database entry overlap between ArrayExpress and GEO is presented as raw counts and a figure (Fig 1).↳ Could also: The same overlap could also be summarized with proportions or a Venn/UpSet diagram alongside the counts, and the fraction unique to each source reported with a 95% confidence interval treating it as a proportion. — Reporting proportions with intervals would add a sense of relative magnitude and sampling uncertainty to the descriptive counts, complementing the absolute numbers.
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Web-server usage is reported as aggregate totals over July 2015–Oct 2019 (visits, page views, hits) plus the statement that two-thirds of visits were under 30 seconds.↳ Could also: Usage could also be summarized as a time series (e.g., monthly visits) with a measure of central tendency and spread, and the session-duration distribution shown as a histogram or with median and IQR. — A temporal and distributional view would convey trends and variability in usage over the multi-year window in addition to the single cumulative totals.
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Example query results are reported as point counts (e.g., 524 items, then 58 records after filtering).↳ Could also: These could also be accompanied by the date/version of the underlying index snapshot and the total indexed corpus size, so counts are expressed as a fraction of the whole. — Anchoring counts to a corpus size and snapshot date would make the numbers reproducible and comparable across database updates.
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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Over 10,000 ArrayExpress data series entries are absent from GEO, demonstrating non-redundant coverage between the two major public gene expression repositories.other ebi-arrayexpress 2020×1papers★ This paper is the founder (earliest)
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Filtering AOE 'hypoxia' results to Homo sapiens and Illumina platform narrows retrieval to 58 data series.other human-illumina-public-databases 2020×1papers★ This paper is the founder (earliest)
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Over 30,000 GEO data series entries are absent from ArrayExpress after reciprocal import was discontinued, leaving a large gap not addressable by either repository alone.other ncbi-geo 2020×1papers★ This paper is the founder (earliest)
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Approximately two-thirds of AOE web server visits lasted under 30 seconds, indicating predominant use for rapid keyword-based dataset look-up rather than extended browsing.other public-gene-expression-databases 2020×1papers★ This paper is the founder (earliest)
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AOE web server accumulated 95,334 visits, 393,174 page views, and 630,837 hits over approximately four years (July 2015–October 2019), indicating sustained community adoption.other public-gene-expression-databases 2020×1papers★ This paper is the founder (earliest)
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Keyword search for 'hypoxia' in AOE retrieves 524 data series integrated across ArrayExpress, GEO, and SRA.other public-gene-expression-databases 2020×1papers★ 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.
Downstream reach in the literature
0 downstream papers · 1 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-31978081 (AOE: All Of gene Expression)
- Title: All of gene expression (AOE): An integrated index for public gene expression databases.
- Author: Bono H. (single author) · PLoS ONE 2020 · DOI 10.1371/journal.pone.0227076
- Code: https://github.com/dbcls/AOE (pinned commit
140ba7fcc81e4370ac007bd94e4441213949cbff, pushed 2022-04-15) - "Data" accession harvested: GEO
GSE52334— see note below (text-mining false positive).
What the paper is
AOE is a resource / index paper. It describes building an index over public
transcriptome databases by harvesting metadata from ArrayExpress (DDBJ MAGE-TAB
mirror), NCBI GEO (via the DBCLS SRA API), and SRA (SRA_Accessions.tab), merging
them into one tab-delimited table, and serving a faceted web front-end (Flask +
D3.js / crossfilter). Methods (verbatim): parsers "implemented in Perl5 and UNIX
shell commands"; visualization "Python3 scripts … D3.js"; usage analytics via
awstats. No statistical analysis, no differential expression, no dimensionality
reduction, no machine-learning pipeline is described.
In-scope vs out-of-scope (per HARD RULE 2)
| Reported result (location) | Pipeline that would produce it | In scope? | Why |
|---|---|---|---|
| ">thirty thousand entries missing in AE", ">ten thousand AE entries missing in GEO" (Results, GEO↔AE comparison) | 02GEO-AE.pl + AE mirror + GEO/SRA API |
NO | Live-DB snapshot at ~2019. No shipped expected value; not pinnable. Upstream ArrayExpress legacy API retired (now BioStudies) → input source gone. A 2026 snapshot vs a 2019 snapshot of a deliberately moving target is not a 1:1 reproduction. |
| Search example "524 items … 58 records (Homo sapiens + Illumina)" (Results / Fig) | live AOE web query | NO | Reflects current DB state of the live site; not a fixed computed value. |
| Usage stats: "95,334 visits, 393,174 page views, 630,837 hits" (Jul 2015–Oct 2019) | author's private awstats server logs |
NO | Not derivable from any shipped data or code (private web-server logs). Flagged per HARD RULE 5 — irreproducible by anyone but the author. (No evidence of fabrication; this is normal private analytics, simply non-auditable.) |
| Nomenclature: "GSE52334 in GEO corresponds to E-GEOD-52334 in AE" | naming convention (s/E-GEOD-(\d+)/GSE$1/) |
trivial | True by construction; a naming rule, not a computational result. This is the only mention of GSE52334 — it is not an analysis dataset. |
| Index schema / column layout (README table, API_documentation.md) | 01AE2REI.pl, 03mkAOEtab.pl |
descriptive | Format spec, no reported numeric value to compare. |
Net: zero in-scope, pinnable, reproducible pipeline outputs.
Why this is a drop (not a partial)
- No expected result that is pinnable — every reported figure is a time-varying snapshot of live external databases or a private web-server log. There is no shipped reference output, no fixed expected value, and the paper itself reports approximate counts ("over thirty thousand") of a moving target.
- Upstream data source retired — the ArrayExpress side of the only quantitative
comparison depends on the legacy ArrayExpress REST/MAGE-TAB service, which has
been migrated to EBI BioStudies; the v3 JSON endpoints the scripts parse now
redirect to
biostudies/arrayexpresshelp. The DDBJ AE mirror + author-internal hosts (gw.ddbj.nig.ac.jp,dtn4,«path»,aoe2018EC2) referenced by99updateAOEall.share unavailable. - Harvested accession is a false positive — GSE52334 is a nomenclature example, not the paper's analysis data; there is no dataset to run a tool over.
- Per the 80/20 rule + "honest 1:1": running the pipeline today would only yield a fresh 2026 snapshot, which is explicitly not a reproduction of the 2019 reported numbers. Producing such a number and grading it would be misleading, so it is not attempted.
Evidence captured
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 correctly-handled DROP (no_expected_result): AOE is a database/index resource paper whose every reported number is either a time-varying live-DB snapshot (>30,000 / >10,000 missing entries, 524 'hypoxia' hits, 58 filtered records) or a private awstats web-usage figure (95,334 visits / 393,174 page views / 630,837 hits), with no shipped reference output to reproduce 1:1. The blocker is on the data-availability/input side — the legacy ArrayExpress API is retired (now BioStudies) and the usage logs are private — not on any core-computation or authors'-integrity failure. The only checkable item, the C8 nomenclature mapping GSE52334↔E-GEOD-52334, is true by construction. No fabrication concern: non-derivability here is the normal nature of a live-resource paper, so overall criticality is yellow rather than red.
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.