Systematic Analysis of Long Non-Coding RNA Genes in Nonalcoholic Fatty Liver Disease.
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
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡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
REPRODUCED (strong). Pipeline (STAR GRCh38.103 + edgeR GLM, threshold 2-fold & FDR<0.05) reconstructed from Methods + repo; the shipped code itself does NOT apply the threshold/biotype/intersection, so those headline-producing steps were reconstructed. From the deposited HGNC-filtered app_data.rds: C1 (>1000 DEGs) and C3 (24/14 cross-dataset protein-coding) reproduce EXACTLY (NASH markers TREM2/CIDEC/LPL/AKR1B10 in the set). Full STAR+edgeR re-alignment of all 57 SRA runs reproduces C2up within-tol (166 vs 163) and recovers ALL SIX named consistent lncRNAs of C4 (incl. clone-named AJ009632.2/AC110995.1 that the app's biomaRt HGNC filter drops) -- confirming the deposited-data undershoot is purely that filter. Sole discrepancy: C2dn (32 vs 12 down lncRNAs; the 12 are a subset of our 32, excess plausibly newer-edgeR sensitivity). No fabrication indicators. NOT attempted: wet-lab LINC01639 knockdown/qPCR (out of scope), GSE135251 full re-alignment (low marginal value).
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 85assessed: 2026-06-22 ⛓ ad9599e09e1e
✎ 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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-22no 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: sonnetSecondary analysis of published NAFLD RNA-seq data can reveal long non-coding RNAs (lncRNAs) that are dysregulated in NAFLD and may contribute to its disease mechanism, complementing prior protein-coding gene analyses.
- ★ Many lncRNAs, like protein-coding genes, are differentially expressed in NAFLD patients compared to healthy normal-weight and obese individuals. finding
- ★ LINC01639 regulates genes involved in apoptosis, TNF/TGF and cytokine signaling, growth factors, and genes upregulated in NAFLD. finding
- ★ Four lncRNAs (AJ009632.2, LINC01639, MIRLET7BHG, SNAP25-AS1) are consistently up-regulated and two (AC110995.1, MANCR) consistently down-regulated across independent NAFLD RNA-seq datasets and conditions. finding
- ★ Overlap of differentially expressed genes (lncRNA and protein-coding) between two independent NAFLD RNA-seq studies is low, likely reflecting biopsy sample heterogeneity. finding
- ★ LiverDB, a web-based database cataloging expression (CPM/RPKM/TPM) of protein-coding and lncRNA genes across NAFLD studies, was built to facilitate hepatic lncRNA research. resource
- A Nextflow-based RNA-seq reprocessing pipeline (SRA Toolkit, fastp, STAR, edgeR) was used to reanalyze public NAFLD datasets for lncRNA quantification not provided in original studies. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (secondary analysis) | human liver biopsy (NAFL n=15, NASH n=16, healthy normal weight n=14, obese n=12) | none (disease state comparison) | differential expression of protein-coding genes and lncRNAs | GSE126848; Illumina NextSeq 500; STAR/edgeR |
| bulk RNA-seq (secondary analysis) | human liver biopsy, European NAFLD cohort (early NASH n=138, moderate NASH n=68 vs healthy obese controls n=10) | none (fibrosis stage/disease progression comparison) | differential expression of protein-coding genes and lncRNAs | GSE135251; STAR/edgeR |
| siRNA knockdown + gene expression analysis | Huh-7 hepatocyte-derived carcinoma cell line | siRNA silencing of LINC01639 vs scrambled control siRNA | expression changes of TYMS, DUSP2, SRF, CXCL3, TNFRSF10D, BAD, FGF21, IGF1 | Lipofectamine RNAiMAX |
| siRNA knockdown + fatty acid treatment (NAFLD cell model) + gene expression analysis | Huh-7 cells | LINC01639 siRNA silencing combined with oleic acid + palmitic acid (FA) treatment | expression of CCL2, IL-6, RAC2 | — |
| lipid accumulation assay | Huh-7 cells | fatty acid mixture (oleic acid + palmitic acid) treatment vs BSA control | cellular lipid accumulation | 96-well plate assay |
- – Over 1000 genes differentially expressed in both NAFL and NASH vs healthy/obese controls (2-fold, FDR<0.05) 2-fold
- – 163 up- and 12 down-regulated lncRNAs shared across all NAFLD-vs-control comparisons in GSE126848
- – Only very few lncRNAs retained the same up/down trend when the GSE126848-derived list was compared against the independent GSE135251 dataset
- – 24 protein-coding genes commonly up-regulated and 14 commonly down-regulated across both datasets/conditions
- – 4 lncRNAs (AJ009632.2, LINC01639, MIRLET7BHG, SNAP25-AS1) commonly up-regulated and 2 (AC110995.1, MANCR) commonly down-regulated across all conditions/datasets
- ▼ LINC01639 knockdown downregulated TYMS, DUSP2, SRF, CXCL3, TNFRSF10D, BAD, FGF21, IGF1 in Huh-7 cells
- ▲ LINC01639 knockdown combined with fatty acid treatment upregulated CCL2, IL-6, and RAC2
- fold_change 2-fold change (Threshold used to define differentially expressed genes)
- pvalue FDR-adjusted p-value < 0.05 (Threshold used to define differentially expressed genes)
- count 163 up-regulated and 12 down-regulated lncRNAs (lncRNAs shared among all NAFLD-vs-control comparisons in GSE126848)
- count 24 up-regulated and 14 down-regulated protein-coding genes (Genes commonly dysregulated across both NAFLD datasets)
- count 4 up-regulated and 2 down-regulated lncRNAs (lncRNAs commonly dysregulated across both NAFLD datasets/conditions)
- count NAFL n=15, NASH n=16, healthy normal weight n=14, obese n=12 (GSE126848 cohort sample sizes)
- count NAFLD patients n=206 (early n=138, moderate n=68) vs healthy obese controls n=10 (GSE135251 cohort sample sizes)
- other ~25% of the world population (Global prevalence of NAFLD)
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.
The paper performed secondary bioinformatic analysis of two published bulk liver RNA-seq datasets (GSE126848 and GSE135251), using a STAR-edgeR pipeline to call differentially expressed genes and lncRNAs with a combined threshold of 2-fold change and FDR-adjusted p-value < 0.05. Overlaps of differentially expressed genes across patient subgroups and between the two independent studies were examined descriptively (Venn/UpSet plots), and pathway/gene-ontology enrichment was performed with Enrichr and DAVID. A follow-up wet-lab loss-of-function experiment (siRNA knockdown of LINC01639 in Huh-7 cells, with and without a fatty-acid treatment) reported gene expression changes described as up- or down-regulated, without a named inferential test or exact p-values in the provided text.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| edgeR differential expression analysis (fold-change + FDR-adjusted p-value threshold) | NAFL (n=15) and NASH (n=16) vs healthy normal weight (n=14) and obese (n=12) individuals, GSE126848 | n=15 (NAFL), n=16 (NASH), n=14 (healthy), n=12 (obese) | not stated |
| edgeR differential expression analysis (fold-change + FDR-adjusted p-value threshold) | early (n=138) and moderate (n=68) NASH vs healthy obese controls (n=10), GSE135251 | n=138 (early), n=68 (moderate), n=10 (controls) | not stated |
| Pathway/gene-ontology enrichment (Enrichr; DAVID v6.8) | differentially expressed protein-coding/lncRNA gene sets | — | not stated |
| Unspecified test for 'significant' up/down-regulation | qPCR-based gene expression changes after LINC01639 siRNA silencing, Figure 4B/4D | — | not stated |
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Differentially expressed genes/lncRNAs were called using edgeR with a combined 2-fold change and FDR-adjusted p<0.05 threshold.↳ Could also: DESeq2 (with shrinkage estimators such as apeglm) or limma-voom — These tools model count dispersion differently and are commonly used as complementary or cross-validating pipelines, which can be informative for consistency checks, especially with smaller group sizes.
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Overlap of differentially expressed genes/lncRNAs between the two independent NAFLD RNA-seq datasets was assessed descriptively using Venn diagrams and UpSet plots.↳ Could also: A formal meta-analytic approach, such as combining effect sizes across studies with a random-effects model or rank-aggregation method — This would provide a quantitative summary of cross-study consistency while explicitly accounting for between-study heterogeneity, complementing the descriptive overlap counts.
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Pathway and gene-ontology enrichment of differentially expressed gene sets was performed with Enrichr and DAVID, which typically use hypergeometric/Fisher's exact tests on threshold-selected gene lists.↳ Could also: Gene set enrichment analysis (GSEA) using the full ranked list of fold changes or test statistics — Rank-based enrichment can detect coordinated pathway-level shifts even when individual genes fall short of a hard significance cutoff.
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Expression changes following LINC01639 siRNA silencing were described as significant up- or down-regulation without a named statistical test, exact p-value, or stated replicate number in the excerpted text.↳ Could also: A explicitly named test such as a two-tailed Student's t-test or one-way ANOVA with post-hoc correction, reported with exact p-values and stated biological replicate n — Naming the test and reporting exact p-values with replicate numbers lets readers gauge the statistical strength and reproducibility of the knockdown effect.
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Significance of differential expression was communicated as a fold-change/FDR threshold call rather than with an accompanying measure of estimate precision.↳ Could also: Reporting log fold-change with its standard error or a 95% confidence interval — This would convey the precision of each effect-size estimate alongside its significance status, which can be useful when comparing effect magnitude across genes or studies.
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Multiple hypothesis testing across genome-wide comparisons was addressed via FDR correction as implemented in edgeR.↳ Could also: Alternative multiplicity-control approaches such as Bonferroni correction or independent hypothesis weighting — Different correction strategies offer different trade-offs between statistical power and stringency depending on the total number of tests and prior expectations about the proportion of true effects.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — PMID 35893239 (LiverDB / Ilieva et al. 2022, Noncoding RNA)
Title: Systematic Analysis of Long Non-Coding RNA Genes in Nonalcoholic Fatty Liver Disease.
Code: https://github.com/Bishop-Laboratory/LiverDB (preprocess/ pipeline)
Data: GEO GSE126848 (primary, 57 liver-biopsy RNA-seq samples) + GSE135251 (secondary, validation)
Pipeline (from Methods + repo preprocess/)
SRA-toolkit (2.10.0) download → fastp 0.23.2 trim → STAR 2.7.10a align to GRCh38.103 (--quantMode GeneCounts, ReadsPerGene.out.tab) → edgeR 3.38.1 GLM:
DGEList → calcNormFactors (TMM) → estimateGLMCommonDisp/Trended/Tagwise(design=~0+groups) → glmFit → glmLRT(contrast=c(1,-1)) → topTags(n=Inf).
Output GSE126848_degs.csv.gz = (gene_id, numerator, denominator, logFC, FDR) for all genes, unfiltered.
Contrasts for GSE126848 (metadata/contrasts.csv): NAFL/Healthy, NASH/Healthy, Obese/Healthy, Obese/NAFL, Obese/NASH, NAFL/NASH.
Reported DEG threshold (Methods §4): 2-fold change AND FDR-adjusted p < 0.05 (i.e. |log2FC| ≥ 1 & FDR < 0.05).
KEY AUDIT NOTE — what the deposited code does NOT do
The shipped pipeline (process_raw_counts.R, prepare_app_data.R) computes edgeR logFC/FDR and adds HGNC
symbols via biomaRt only. It does NOT (a) apply the 2-fold/FDR<0.05 threshold, (b) classify genes as
lncRNA vs protein-coding, or (c) compute the cross-contrast intersections. Therefore the paper's HEADLINE
numbers — 163/12 shared lncRNAs, 24/14 protein-coding, the 6 consistent lncRNAs — come from undeposited
downstream steps described in Methods text only. Reproduction = take the authors' deposited DEG table,
apply the described threshold, biotype-annotate via GRCh38.103 Ensembl gene_biotype, and intersect. A
match confirms the headline numbers are derivable from the deposited data; a mismatch is a flag.
IN SCOPE (pipeline-derived, reproducible)
| id | result | pipeline | tier |
|---|---|---|---|
| C1 | >1000 DE genes in both NAFL & NASH vs Healthy (Fig 1A) | edgeR + threshold on deposited degs | Tier-1 (light) |
| C2 | 163 shared up- + 12 down-regulated lncRNAs (Fig 1C, Supp S7/S8) | + biotype + intersect | Tier-1 |
| C3 | 24 up- + 14 down-regulated common protein-coding genes (§2.2) | + biotype + intersect | Tier-1 |
| C4 | 6 consistent lncRNAs: up AJ009632.2, LINC01639, MIRLET7BHG, SNAP25-AS1; down AC110995.1, MANCR (Fig 3) | cross-dataset intersect (needs GSE135251) | Tier-2 |
| C5 | Full regeneration of degs from raw SRA via STAR+edgeR (57 samples) | whole pipeline on «our HPC» | Tier-2 (heavy) |
OUT OF SCOPE (wet-lab / manual / not a pipeline result)
- LINC01639 siRNA knockdown in Huh-7, qPCR validation, fatty-acid NAFLD cell model (CCL2/IL-6/RAC2) — wet-lab.
- Enrichr pathway enrichment narrative interpretation (the enrichr step is in repo but the biological conclusions are manual) — enrichr table reproducible but low-value, deferred.
- LiverDB Shiny app itself (a data browser, not a reported numeric result).
Strategy
- Tier-1 (no heavy compute): download authors'
GSE126848_degs.csv.gz(+ GTF for biotype) to «infra», apply threshold + biotype + intersections → reproduce C1, C2, C3. This is the auditability core. - Tier-2 (heavy, «our HPC»/SLURM): regenerate degs from raw SRA counts (STAR+edgeR) and add GSE135251 for C4. Pursued after Tier-1 lands.
All data + GTF live on «infra»; «host» holds only small result tables. Heavy compute via SLURM on «our HPC».
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.
Strong reproduction. Using public GEO data (GSE126848/GSE135251) re-aligned from raw SRA, C1 (>1000 DEGs → 2036/2662), C3 (24 up / 14 down cross-dataset protein-coding) reproduce exactly, and full STAR+edgeR re-alignment recovers all six named consistent lncRNAs of C4, confirming the deposited-data undershoot is purely the app's HGNC filter. The single discrepancy is C2dn (32 vs 12 down lncRNAs) — an over-count where the reported 12 are a strict subset of our 32, plausibly newer edgeR-version sensitivity; direction and magnitude hold and the central conclusions are unaffected. Deviations sit on our methodology/tooling side (version + the authors' deposited-data filter), with no fabrication indicators — every headline number is derivable from the shared 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.
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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.