ASTRA: a comprehensive resource of stress-induced transcriptional activity in human cell lines.
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.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓The central claim held under reproduction
- 🟡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
- 🟡Overall, the reproduction showed a material discrepancy
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 from the paper's own shipped data, with caveats. The GitHub repo is only the Streamlit web-app frontend (no pipeline code), but the Zenodo deposit (10.5281/zenodo.15885686) ships the pipeline OUTPUTS (TPM matrix, edgeR DE table, biomaRt annotations, GEO metadata), so per BRIEF rule P16 we reproduced against those. RESULT = 1:1 on the headline curation counts: 669 samples, 59 studies, 57 cell lines, 46 publications, and the described coding/lncRNA/pseudogene biotype coverage all reproduce EXACTLY from the shipped metadata. The core DE output is validated by internal consistency: recomputing per-gene fold-change directly from the shipped TPM matrix reproduces the SIGN of the shipped edgeR log2FC for ~95-100% of DE-significant genes across 8 GSEs spanning all 4 stress types (Pearson 0.23-0.85; lower Pearson expected because edgeR uses counts+TMM+NB-GLM vs our crude TPM-ratio) - consistent with a genuinely-derived, non-fabricated DE table. DIVERGENCES (honest): (a) the reported '86 differential-expression comparison sets' is NOT recoverable from the deposit - the DE file is keyed only by gse_id with no comparison-set identifier, covers only 45 of 59 studies (14 missing entirely), and yields ~81 stacked sets, while datasets.tsv lists 185 control/treatment contrasts; (b) the per-stress split 20/17/16/33 matches neither study-level (15/9/15/20) nor contrast-level (39/52/27/67) counts; (c) 27 genotypes shipped vs 26 reported. NOT ATTEMPTED (the >20% tail): the full STAR+RSEM+edgeR re-run from raw FASTQ - terabytes of reads, GRCh38 index build, and incompletely-documented per-comparison sample grouping; the internal-consistency check stands in as a lighter validation of the DE step. All raw/large data kept on «infra»; only small derived results on «host».
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 73assessed: 2026-06-15 ⛓ 007f030898a4
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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 2026-06-16no 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: opusKnowledge of cellular stress response pathways is fragmented; ASTRA was developed to provide a curated, standardized, open-access database of human cell line transcriptomic responses to defined molecular stressors, enabling systematic discovery of conserved and stress-specific coding and noncoding stress-responsive genes and RNA biomarkers.
- ★ ASTRA is a curated, open-access database cataloging standardized bulk RNA-seq transcriptomic responses to stress in human cell lines, organized into four stress categories (oxidative, hypoxia, heat shock, DNA damage). resource
- ★ A harmonized, standardized computational pipeline (QC, alignment, normalization, differential expression) ensures comparability across samples and studies. method
- ★ ASTRA profiles both coding (mRNA) and long noncoding RNA/pseudogene transcriptomes to capture the noncoding genome's contribution to the stress response. resource
- ★ Each dataset pairs stressed and matched control samples annotated with detailed metadata (cell line identity, tissue origin, cell-state, treatment parameters, sequencing protocols). resource
- ★ The web interface enables gene-level expression queries, differential expression analyses, and visualization of transcriptional dynamics across datasets, conditions, and cell-type groups. method
- ASTRA supports identification of novel stress-responsive genes and RNA-mediated regulatory mechanisms for functional genomics, systems biology, and translational research. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (oxidative stress, H2O2 50–700 μM) | human cell lines | H2O2 treatment | mRNA and lncRNA expression (TPM) and differential expression | — |
| bulk RNA-seq (hypoxia, 1%–3% oxygen) | human cell lines | low oxygen exposure | mRNA and lncRNA expression (TPM) and differential expression | — |
| bulk RNA-seq (heat shock, 39°C–45°C) | human cell lines | increased temperature | mRNA and lncRNA expression (TPM) and differential expression | — |
| bulk RNA-seq (DNA damage, UV 5–60 J/m2) | human cell lines | UV radiation | mRNA and lncRNA expression (TPM) and differential expression | — |
- – 59 GEO studies were curated and used to finalize 86 datasets (comparison sets) 59 studies, 86 datasets
- – Datasets distributed as 33 oxidative stress (H2O2), 20 heat shock, 17 UV, and 16 hypoxia studies 33/20/17/16
- – 669 RNA-seq samples retrieved and systematically processed through the standardized pipeline 669 samples
- – Datasets encompass 57 unique cell lines, 26 genotypes, 40 time points, and 27 dosage levels 57 cell lines; 26 genotypes; 40 time points; 27 doses
- – Cell lines grouped into three cell-states: non-diseased reference, cancer, and organoid/3D 3 groups
- count 59 (number of studies cataloged in ASTRA)
- count 86 (within-study differential expression comparison sets (datasets))
- count 669 (individual RNA-seq samples processed)
- count 33 (oxidative stress (H2O2) studies)
- count 20 (heat shock response studies)
- count 17 (UV response studies)
- count 16 (hypoxia studies)
- count 57 (unique cell lines cataloged by tissue of origin)
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.
ASTRA is a database and computational pipeline paper that integrates and reprocesses 669 publicly available human RNA-seq samples from 59 GEO studies into 86 stressed-vs-control comparison sets across four stress categories. Differential expression between stressed and matched control samples was computed with edgeR; transcript abundance was quantified as TPM via RSEM. Pairwise co-expression relationships in the interactive interface are summarized with both Pearson's r and Spearman's ρ. The paper reports gene-level fold-change and average p-value summaries across datasets as a resource tool rather than as a single confirmatory experiment.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| edgeR differential expression (negative binomial GLM) | Treated (stressed) vs untreated (control) comparisons within each of the 86 within-study comparison sets | 669 total samples across 86 comparison sets; per-dataset sample sizes not individually reported in the text | not stated |
| Pearson's r correlation with P-value | Pairwise gene co-expression within a stress type (Gene Search / Within Stress-Type tab) | — | not stated |
| Spearman's ρ correlation with P-value | Pairwise gene co-expression within a stress type (Gene Search / Within Stress-Type tab) | — | not stated |
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Differential expression was computed with edgeR across all 86 comparison sets↳ Could also: DESeq2 (negative binomial GLM with a different shrinkage estimator) or limma-voom (variance-stabilized linear model) could also be applied to RNA-seq count data — DESeq2 and limma-voom are widely used alternatives; running more than one tool and comparing the overlap of DE calls is common practice in large-scale reanalysis pipelines to assess robustness across methods
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Transcript abundance was normalized and reported as TPM via alignment-based RSEM quantification↳ Could also: Pseudo-alignment tools such as kallisto or Salmon could also produce TPM estimates with substantially faster runtimes at comparable accuracy; TMM-normalized counts (edgeR's native scaling) could also serve as the expression unit — Pseudo-alignment approaches are increasingly common in large-scale pipelines; TMM-normalized counts align directly with edgeR's internal model assumptions and can simplify the transition from quantification to DE analysis
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Cross-study comparability was addressed through a standardized processing pipeline; no explicit statistical batch-correction step is described↳ Could also: Methods such as ComBat-seq, RUVSeq, or limma's removeBatchEffect could also be applied to reduce technical variation attributable to study of origin before pooling expression values for cross-dataset visualization — When aggregating data from many independent labs, platforms, and protocols, explicit batch-effect estimation and correction can reduce study-of-origin confounding in cross-dataset expression comparisons
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Both Pearson's r and Spearman's ρ are reported for co-expression analysis without designating one as primary↳ Could also: Designating Spearman's ρ as the primary metric (with Pearson's as supplementary) is also common; mutual information or distance correlation could additionally capture non-linear dependencies — TPM distributions are typically right-skewed and contain zeros; rank-based Spearman's ρ is robust to these features, and making the primary choice explicit aids reproducibility and interpretation for database users
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The type of p-value used for DEG filtering (raw vs FDR-adjusted) is not explicitly stated in the text↳ Could also: Explicitly applying and documenting Benjamini-Hochberg FDR correction within each comparison set, with the correction scope stated, is also standard practice for RNA-seq DE analysis — Clearly specifying whether thresholds apply to raw or adjusted p-values, and the scope of the correction (per-dataset gene family), makes the DEG filtering criteria unambiguous and reproducible for downstream users of the resource
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Dataset inclusion was determined by manual curation criteria applied to GEO studies↳ Could also: Reporting inter-rater reliability (e.g. Cohen's κ) for manual curation decisions and/or explicit automated QC thresholds (e.g. minimum alignment rate, minimum library size) could also be documented alongside the curation criteria — Quantifying the reproducibility of manual inclusion decisions and making automated quality thresholds explicit helps users understand the consistency of the curation process and the boundaries of the resulting resource
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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-41263110 (ASTRA)
Paper: ASTRA: a comprehensive resource of stress-induced transcriptional activity in human cell lines. Nucleic Acids Res 2026. PMID 41263110 · PMCID PMC12807686 · DOI 10.1093/nar/gkaf1174.
Resource type: a curated transcriptomics database (web app + data deposit), not a wet-lab study. The "results" are database contents and pipeline-derived differential-expression tables.
Pipeline described in Methods
Raw RNA-seq (GEO/ENA) → FastQC → adapter detection (Minion/Reaper) + Cutadapt → STAR (→ GRCh38) → RSEM (TPM quantification) → edgeR (treated vs untreated DE) → biomaRt/Ensembl BioMart annotation (Bioconductor 3.22). Output: per-comparison DE tables (log2FC, p-value) + a TPM expression matrix, served by a Streamlit + PostgreSQL app.
Code artifact (P16 note)
The GitHub repo Mtsif/astra-db is the web-app frontend (Streamlit + Postgres
badges, MIT). It ships no read-processing/DE pipeline code. The pipeline itself
(STAR/RSEM/edgeR) is described in Methods but not shipped as runnable code. Per
BRIEF rule P16, applying the described standard tools to the paper's own data is an
equally valid reproduction.
Shipped data (Zenodo 10.5281/zenodo.15885686, CC-BY-4.0, ~1.5 GB)
expression_data.tsv(1.32 GB) — TPM expression matrix (long format)differential_expression.tsv(92 MB) — edgeR DE results: log2FC + p-value per gene × comparison (the core pipeline output)sample_metadata.tsv(2.0 MB) — per-sample metadatastudy_metadata.tsv(130 KB) /datasets.tsv(28 KB) — study indexharmonized_dataset.tsv(77 KB) — harmonized metadatagene_annotations.tsv(7.1 MB) — biomaRt gene annotations
IN SCOPE (reproducible from shipped data — what we attempt)
- Headline counts (pipeline/curation-derived, stated in abstract & Fig/Table): 669 samples · 59 studies · 86 DE comparison sets · 57 unique cell lines · 46 publications · stress-type split (20 heat shock / 17 UV / 16 hypoxia / 33 oxidative). Verify directly against the shipped metadata + DE deposit.
- DE-table internal consistency / reproduction: the deposit ships both the TPM matrix and the edgeR DE table. Recompute fold-changes from the shipped TPM matrix and correlate with the shipped edgeR log2FC across several comparisons. This reproduces the differential-expression output from the paper's own data and detects whether the DE table is faithfully derived (vs fabricated/inconsistent).
OUT OF SCOPE / the hard 20% (not fully attempted — say why)
- Full STAR+RSEM+edgeR from raw FASTQ for all 669 samples — terabytes of FASTQ, GRCh38 index build, weeks of alignment. Not feasible and explicitly the >20% tail. We may attempt one comparison end-to-end if a small, clearly-mapped study surfaces, but the headline reproduction stands on the shipped deposit (1 & 2).
- Web-app/Streamlit deployment, browser-compatibility testing — non-pipeline.
- Wet-lab / stressor-dosage curation choices — manual, not computational.
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.
The headline resource claims reproduce 1:1 from the authors' own Zenodo deposit — 669 samples, 59 studies, 57 cell lines, 46 publications, and the described coding/lncRNA/pseudogene biotype coverage — and the shipped edgeR DE table is internally consistent with the shipped TPM matrix (sign agreement ~95-100% across 8 GSEs), strong evidence it is genuinely derived. The substantive divergences are on the authors'/deposit side: the reported 86 DE comparison sets and their 20/17/16/33 stress split are not derivable (DE table keyed only by gse_id, covers only 45/59 studies; recomputation gives 81/45/185, none=86), and genotypes are 27 vs 26. These are moderate, well-explained under-documentation issues, not fabrication, and the central conclusion of a comprehensive, genuinely-curated resource holds. Overall yellow: solid reproduction with explainable, documentation-driven deviations.
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
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