A comparison of the large-scale gene expression patterns in summer and fall migratory Pantala flavescens (Fabricius) in northern China.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Any deviation was negligible
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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, but compute did NOT run this session. Paper: de-novo transcriptome of Pantala flavescens, summer (M7/July) vs fall (M10/Oct). The brief's 'code' is the third-party tool fastp (P16-valid); the clear low-hanging target is Table 2 (fastp QC). I solved the non-trivial run-selection step: BioProject PRJNA762591 has 24 runs but the paper used 6, and the paper does not state which. The paper's Table 2 'Raw reads' equal EXACTLY 2x the ENA-archived read-pair counts for runs MF7a/b/c+MF10a/b/c (integer-exact on all 6 samples; e.g. M7_1 48,371,764 = 2x24,185,882), which both identifies the 6 runs and confirms raw counts are faithful to the archive -> 6 raw-read claims graded exact (verified from public ENA metadata, no «our HPC» needed; no fabrication concern). The fastp step (clean reads/Q20/Q30/GC = rest of Table 2) was fully prepared (run.sbatch with fastp default params on the 6 runs + compare.py auto-grader) but could NOT be submitted: all compute must run on «our HPC» and the «our HPC» VPN required interactive 2FA approval (rotating ~2-3 min challenge link) that was not completed in the session window -- an infrastructure/operator-availability blocker, NOT a paper-side reproducibility defect (repo public, data public, expected values identifiable). NOT attempted by design (80/20): Trinity+TGICL assembly (17810 unigenes/N50 3583), annotation, DESeq2 DEGs (624; 352 up/272 down), GO/KEGG enrichment, qRT-PCR -- the de-novo assembly chain is non-deterministic with under-specified params/DB versions. To finish: scp run.sbatch+compare.py to «infra», sbatch, then compare.py grades clean-reads/Q20/Q30/GC vs Table 2. See scope.md, AUDIT.md, agreement.json, claims.tsv.
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
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v1 current initial assessment Score 93assessed: 2026-06-15 ⛓ af3be8d6015a
✎ I am an author of this paper
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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
- 2026-09-19
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: sonnetThe study investigates whether gene expression profiles differ between summer (northward) and fall (southward) migratory Pantala flavescens, aiming to identify genes and pathways underlying physiological adaptation to different seasonal/climatic migration conditions.
- ★ 624 differentially expressed genes (DEGs) were identified between summer (M7) and fall (M10) migratory P. flavescens finding
- ★ Several DEGs (cpr49Ae, itm2b, chitinase, cpr11B, laccase2, nd5, vtg2) correspond to genes previously linked to cold- and high-temperature resistance finding
- ★ Antibacterial humoral response, response to bacteria, and lipid transporter activity pathways are significantly enriched in summer migrants finding
- ★ Structural constituent of cuticle, chitin binding, mitochondrion, propanoate metabolism, citrate cycle, and hypertrophic cardiomyopathy pathways are significantly enriched in fall migrants finding
- ★ qRT-PCR of 10 selected DEGs confirmed the reliability of the RNA-seq data method
- ★ De novo transcriptome assembly produced 17,810 unigenes and 27,701 transcripts as a reference resource for P. flavescens resource
- ★ Enhanced mitochondrial/energy metabolism gene expression in fall migrants reflects increased energy release needed under cold stress mechanism
- Muscle contraction-related genes (mhc, actin, mca-3) are upregulated in fall migrants, consistent with cardiomyopathy pathway enrichment finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (Illumina, de novo transcriptome assembly) | Pantala flavescens, thoracic muscle, wild-caught summer (M7) and fall (M10) migrants, Beihuang Island, China | none (natural seasonal migration comparison) | gene expression levels (RPKM), differentially expressed genes | Illumina sequencing; Trinity, TGICL, Bowtie2, RSEM, DESeq2 |
| quantitative real-time PCR (qRT-PCR) | Pantala flavescens, thoracic muscle, M7 and M10 individuals | none | relative expression of 10 selected DEGs (2^-ΔΔCt method) with β-actin reference | LightCycler 480 Real-time PCR system (Roche), ChamQ Universal SYBR qPCR Master Mix |
| transcriptome annotation (BLAST against nr, nt, KEGG, GO, Swiss-Prot, COG databases) | Pantala flavescens assembled unigenes | none | functional annotation and putative gene identity | BLAST v2.2.23, Blast2GO v2.5.0, WEGO |
| GO and KEGG functional enrichment analysis | Pantala flavescens DEG set (M7 vs M10) | none | enriched GO terms and KEGG pathways | Goatools, KOBAS |
- – 624 DEGs identified between M7 and M10 (352 upregulated, 272 downregulated in M7 vs M10) |log2FC| ≥ 2, FDR ≤ 0.01
- ▲ Top 15 upregulated genes mostly annotated as structural constituent of cuticle (11/15) up to 1665-fold (FC M10/M7)
- ▼ Top 15 downregulated genes mainly vitellogenin/lipid transporter activity (6/15), defensin/sarcotoxin (3/15), ion binding (3/15)
- – 10 GO terms significantly enriched: cuticle constituent, chitin binding, lipid transporter activity, iron-ion binding (MF); mitochondrion, extracellular region (CC); response to bacterium, humoral response (BP)
- – 4 KEGG pathways significantly enriched: hypertrophic cardiomyopathy, propanoate metabolism, citrate cycle, dilated cardiomyopathy
- ▼ Chitinase expression downregulated in fall migrants
- ▲ vtg2 (vitellogenin) and fasn2 (fatty acid synthase 2) upregulated in summer migrants under lipid transporter activity term
- ▲ Energy metabolism genes (mtpbeta, dld-1, hibch, pdha) upregulated in fall migrants
- count 17,810 unigenes; 27,701 transcripts (de novo transcriptome assembly output)
- count 624 DEGs (DEGs between M7 and M10 groups)
- count 352 upregulated / 272 downregulated (direction breakdown of 624 DEGs (M7 vs M10))
- fold_change 1665.035 (highest FC, TRINITY_DN2090_c0_g1) (top upregulated gene, glycine-rich cell wall structural protein, corrected p=1.99E-11)
- pvalue corrected p-value ≤ .01 with |log2 ratio| ≥ 2 (DEG significance threshold (DESeq2))
- count 10,920 unigenes annotated (61.32%) (unigenes with annotation in at least one database)
- pvalue .0027 (GO enrichment: structural constituent of cuticle and chitin binding)
- mean average unigene length 2046 bp; N50 = 3583 bp (transcriptome assembly quality metrics)
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 two-group (summer vs. fall migration) comparative transcriptomic study used de novo assembly of Illumina RNA-seq data from pooled biological replicates (n=3 per group, each pool comprising 5 individuals) to identify differentially expressed genes (DEGs) via DESeq2. Significance was declared at |log2FC| ≥ 2 and FDR-adjusted p ≤ 0.01; GO and KEGG pathway enrichment analyses were performed with corrected p-values. Ten DEGs were validated by qRT-PCR (2^(−ΔΔCt) method, n=3 biological replicates, β-actin reference, SPSS v16.0), with results summarised as fold-change with standard deviation error bars.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 negative binomial Wald test | DEG identification: summer migrants (M7) vs. fall migrants (M10) across 17,810 unigenes | 6 samples total (3 biological replicates per group, each pooled from 5 individuals) | not stated |
| GO enrichment test (Goatools; underlying test not specified in text) | GO term enrichment of DEGs, M7 vs. M10 | 624 DEGs against annotated background (size not explicitly stated) | not stated |
| KEGG pathway enrichment test (KOBAS; underlying test not specified in text) | KEGG pathway enrichment of DEGs, M7 vs. M10 | 624 DEGs against KEGG-annotated background (size not explicitly stated) | not stated |
| 2^(−ΔΔCt) relative quantification with unspecified inferential test (SPSS v16.0) | qRT-PCR validation of 10 selected DEGs | 3 biological replicates per group | not stated |
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Each biological replicate was constructed by pooling RNA from five individuals before library preparation, yielding three pooled replicates per group↳ Could also: Individual-level replicates (one library per individual, more individuals per group) could also be used — Individual replicates allow DESeq2 (and similar tools) to estimate within-group biological variance directly from individuals rather than from pooled variance, which can improve dispersion estimation and increase statistical power when between-individual expression variability is substantial
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DESeq2 was used with n=3 biological replicates per group, the minimum recommended for its variance estimation↳ Could also: edgeR (quasi-likelihood F-test) or limma-voom could also be applied to the same count matrix — With very small n, these tools offer alternative dispersion-shrinkage strategies; limma-voom in particular applies empirical-Bayes variance smoothing that some studies report as conservative and well-calibrated at n=3, providing an independent cross-check on DESeq2 findings
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A single reference gene (β-actin) was used to normalise qRT-PCR data via the 2^(−ΔΔCt) method↳ Could also: Normalisation to the geometric mean of two or more validated reference genes (e.g., β-actin plus RPS18 or EF1α) could also be used — Multi-gene reference normalisation reduces measurement error when any single reference gene is not perfectly stable across experimental conditions; MIQE guidelines recommend validating reference gene stability (e.g., with geNorm or NormFinder) before use
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The specific inferential statistical test applied to qPCR data in SPSS is not named in the text↳ Could also: A two-sample t-test or Mann-Whitney U test (for small n) on ΔCt values between M7 and M10 could be explicitly named and reported — Naming the test and reporting the test statistic alongside the p-value makes the validation analysis independently reproducible and allows readers to assess distributional assumptions for n=3 per group
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GO and KEGG enrichment analyses report 'corrected p values' without specifying the correction method or the background gene set used↳ Could also: Explicitly stating the correction method (e.g., BH-FDR), the background (all detected unigenes vs. full annotated genome), and the underlying test (e.g., Fisher's exact or hypergeometric) would also be standard practice — Different background definitions and correction methods can materially change which terms reach significance; reporting these choices allows readers to judge the scope of the enrichment and to reproduce the analysis
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Enrichment analysis was performed on the discrete list of 624 DEGs defined by a |log2FC| ≥ 2 and padj ≤ 0.01 cut-off↳ Could also: Gene Set Enrichment Analysis (GSEA) using the full ranked list of all expressed genes (ranked by log2FC or Wald statistic) could also be applied — GSEA does not require a binary DEG cut-off and uses the complete expression gradient, which can detect coordinated but modest shifts in pathway activity that threshold-based over-representation methods may miss; it is widely used in comparative transcriptomic studies as a complementary approach
Citation network
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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-39108562
Paper: Cao L, Wang N. A comparison of the large-scale gene expression patterns in summer and fall migratory Pantala flavescens (Fabricius) in northern China. Ecol Evol 2024. PMID 39108562 / PMC11301579 / DOI 10.1002/ece3.70147.
Code link in brief: https://github.com/OpenGene/fastp (third-party tool — P16: applying an existing third-party tool to the paper's own data is equally valid).
Data: SRA BioProject PRJNA762591.
Design / data disambiguation (important)
The BioProject PRJNA762591 contains 24 runs (sample aliases of the form
[M/L][M/F][7/8/10][a-c]), but the paper used only 6 (3 summer "M7" + 3 fall
"M10", each a pooled RNA library of 5 individuals). The paper's Table 2 totals
(316.79 M clean reads; per-sample 48.0–55.5 M; i.e. ~24–28 M read pairs × 2)
match the MF7a/b/c + MF10a/b/c runs, not the MM*/LM*/LF* ones:
| paper label | SRA run | alias | read pairs (ENA) | ×2 (≈clean reads) |
|---|---|---|---|---|
| M7-1 | SRR15962836 | MF7a | 24,185,882 | 48.4 M |
| M7-2 | SRR15962835 | MF7b | 26,900,236 | 53.8 M |
| M7-3 | SRR15962833 | MF7c | 26,326,613 | 52.7 M |
| M10-1 | SRR15962839 | MF10a | 27,479,211 | 55.0 M |
| M10-2 | SRR15962838 | MF10b | 28,004,978 | 56.0 M |
| M10-3 | SRR15962837 | MF10c | 26,844,902 | 53.7 M |
Sum of raw pairs ×2 ≈ 319.5 M (raw reads) → after fastp ~316.79 M clean (paper). The MM7/MM10 alternative sums to only ~289 M, which cannot yield 316.79 M clean reads, so MF* is the correct mapping. (Provisional — a human reviewer should confirm the alias→paper-label assignment.)
IN SCOPE (pipeline-derived, attempted)
- Table 2 — fastp QC metrics (the named tool, default parameters): per-sample
clean reads, total clean reads (316.79 M), Q20 (98.3–98.45 %), Q30
(94.74–95.14 %), GC (35.81–40.53 %). Pipeline:
fastpdefault params on each paired run. This is the clear, low-hanging 1:1 reproduction.
OUT OF SCOPE / NOT ATTEMPTED (the hard ~20%, per 80/20 rule)
- De-novo assembly stats (Trinity + TGICL v2.1 → 17,810 unigenes / 27,701 transcripts / avg 2046 bp / N50 3583 bp). De-novo assembly of ~320 M reads is heavy and non-deterministic; TGICL redundancy clustering + parameters are under-specified; output is not bit-reproducible. Not attempted.
- Annotation counts (Table 3: 10,920 annotated, GO 8,135, KEGG 7,189) — depend on the assembly above + external DB versions (nr/nt/KEGG/GO/Swiss-Prot/COG, versions unstated). Not attempted.
- DESeq2 DEGs (624 total; 352 up / 272 down, |log2FC|≥2, padj≤0.01) — depend on the assembled reference (Bowtie2 + RSEM RPKM). Not attempted (downstream of the non-reproducible assembly).
- GO/KEGG enrichment (Tables 4/5) — downstream of DEGs. Not attempted.
- qRT-PCR validation — wet-lab, out of scope.
Rationale
fastp on the paper's own runs reproduces Table 2 exactly the way the paper produced it (same tool, default params, public data) and additionally lets us verify which 6 of 24 runs the paper used — a clean, auditable data point. The assembly→DEG chain is intentionally not chased (80/20).
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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.