Core transcriptional signatures of phase change in the migratory locust.
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.
- ✓The central claim held under reproduction
- 🟡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
- 🟡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 the DOWNSTREAM pipeline from deposited data. This is a P16 case: the brief 'Code' link (preprocessCore) is a generic normalization library, not the authors' analysis pipeline, and no authors' repo exists; the ~6.5 Gb L. migratoria genome makes raw-read re-alignment prohibitive, so we reproduced from the deposited supplementary RPKM matrices (Table S2) and ranked gene tables (Table S3/S8). EXACT: 129 samples and 97.36% gene coverage; and the Borda aggregation step reproduces 1700/1700 (final rank == interleaved |Borda_Score|, 850 phase-up + 850 phase-down). WITHIN-TOL: 1,700 = top-10% cutoff (1,712 theoretical rounded), and LOO-CV phase accuracy 91.7% (22/24) vs reported 87.5% (21/24) -- crucially, raw-RPKM classification of tissue-paired phase is 0% while AC-PCA-corrected is 83-100%, confirming the paper's central methodological claim that AC-PCA correction makes the 1,700 PhaseCore genes phase-predictive. PARTIAL: AC-PCA PC1 loadings correlate 0.51/0.64 with deposited values (vs 0.03 for ordinary PCA), confirming the confounder-removal mechanism though not the exact loadings (acPCA v1.2 exact lambda/scaling not matched). NOT REPRODUCIBLE (honest gap): RNAi DEG counts (251/171/417) require edgeR on raw counts that were not deposited (only RPKM). NOT ATTEMPTED (out of scope, too heavy/under-specified): raw-read alignment to the 6.5 Gb genome, the 926 TF/94 family annotation, and the 10,024-node/15,009-edge TRN built from 8 ensemble network methods. No value was forced or fabricated; all grades are provisional for a human auditor.
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 60assessed: 2026-06-16 ⛓ cabe0a3edfe1
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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
- 2026-06-16
- 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: opusDo core transcriptional signatures (a small core set of genes and transcription factors) exist across spatiotemporal scales that globally regulate density-dependent phase change (a typical phenotypic plasticity) in the migratory locust?
- ★ PhaseCore genes defined by AC-PCA contribution to phase differentiation predict phase status with >87.5% accuracy finding
- ★ PhaseCore genes display unique gene attributes: faster evolution rate, higher CpG content, higher specific expression, lower methylation, lower network connectivity, and enrichment for phase-related DEGs finding
- ★ 20 transcription factors (PhaseCoreTF genes) are associated with the regulation of PhaseCore genes finding
- ★ Three representative TFs (Hr4, Hr46, grh) regulate locust phase change, verified experimentally by RNAi mechanism
- ★ AC-PCA removes confounding factors and its PC1 classifies samples into gregarious and solitary phases where conventional PCA and PLS fail method
- A genome-wide transcriptional regulatory network was built by combining eight TRN reconstruction methods with an ensemble approach method
- LocustMine online resource provides access to the expression and network data resource
- Core transcriptional signatures suggest a potential common mechanism underlying phenotypic plasticity in insects mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq (developmental dataset) | Locusta migratoria, egg to adult developmental stages | none (phase comparison gregarious vs solitary) | gene expression (RPKM) | — |
| bulk RNA-seq (tissue dataset) | Locusta migratoria, eight tissues/organs (brain, thoracic ganglia, antennae, wing, pronotum, fat body, hemolymph) | none (phase comparison) | gene expression (RPKM) | — |
| bulk RNA-seq (time course datasets) | Locusta migratoria, brain and thoracic ganglia tissues at 6 time points (0,4,8,16,32,64 h) | gregarization (crowding of solitary, CS) and solitarization (isolation of gregarious, IG) | gene expression over time course | — |
| RNAi knockdown + behavioral assay | Locusta migratoria, fourth-instar gregarious nymphs | dsRNA knockdown of Hr4, Hr46, grh (GFP control) | Pgreg behavior score, total distance moved, total duration of movement | — |
| bulk RNA-seq (post-RNAi) | Locusta migratoria, brain tissue of gregarious locusts | RNAi knockdown of Hr4, Hr46, grh vs GFP control | differentially expressed genes | — |
| computational AC-PCA / Borda aggregation / cross-validation | integrated locust transcriptomic datasets (reference gene set 17,586 genes) | none | PC1 ranking, prediction accuracy (LOO-CV, CDV) | — |
| transcriptional regulatory network reconstruction (ensemble of 8 methods) | Locusta migratoria, 129 heterogeneous samples (48 + 81) | none | TF-target edges/network nodes | — |
- – AC-PCA PC1 cleanly separated gregarious vs solitary samples across all three datasets, unlike conventional PCA/PLS PC1 vs PC2 variance: 3.8% vs <0.001% (development), 1% vs <0.001% (tissue), 9.4% vs 6.2% (time course)
- – 1,700 PhaseCore genes defined using top 10% cutoff of Borda list; LOO-CV prediction accuracy was 87.5% 87.5% accuracy; 1,700 genes
- ▲ PhaseCore genes significantly cover more DEGs than other genes in Brain_Hou and Pronotum_Yang studies hypergeometric test P < 1e-70 for both
- – 926 TF genes identified in 94 families; 33 TF genes among PhaseCore genes; 52.9% (n=490) of TF genes are PRGs 926 TFs, 94 families, 52.9% PRGs
- – Genome-wide TRN contained 10,024 nodes connected by 15,009 edges, covering 873 TF genes and 986 PhaseCore genes; each TF regulated 17.2 targets on average 10,024 nodes / 15,009 edges; 17.2 targets/TF
- ▲ 20 PhaseCoreTF genes identified; PhaseCoreTFs have higher-ranked PC1 and higher PRG proportion than non-PhaseCoreTFs Mann-Whitney P=1e-5 (PC1); binomial P=2.7e-5 (PRG)
- ▼ RNAi of Hr4, Hr46, grh shifted behavior toward solitary (reduced Pgreg) and suppressed locomotor activity Hr4 P=0.024; Hr46 and grh P<0.005 (Mann-Whitney)
- – RNAi of Hr4, Hr46, grh produced 251, 171, and 417 DEGs respectively vs GFP control; 124 DEGs regulated by ≥2 TFs 251 / 171 / 417 DEGs; 124 shared
- other 87.5% accuracy (LOO-CV prediction accuracy using 1,700 PhaseCore genes)
- pvalue P < 1 × 10−70 (hypergeometric test, PhaseCore genes cover more DEGs (both Brain_Hou and Pronotum_Yang))
- pvalue P = 1 × 10−5 (Mann-Whitney test, PhaseCoreTF genes higher-ranked PC1 values)
- pvalue P = 2.7 × 10−5 (binomial test, higher PRG proportion in PhaseCoreTF genes)
- pvalue P = 0.024 (Mann-Whitney test, Hr4 RNAi reduced Pgreg toward solitary)
- pvalue P < 0.005 (Mann-Whitney test, Hr46 and grh RNAi behavioral change)
- count 926 TF genes in 94 families (genome-wide TF identification; 33 among PhaseCore genes)
- count 10,024 nodes / 15,009 edges (final genome-wide transcriptional regulatory network)
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 an integrative/meta-analysis of multiple migratory-locust transcriptomic datasets (developmental, tissue, and time-course RNA-seq) using adjustment-for-confounding PCA (AC-PCA) to rank genes by their contribution to phase difference, Borda rank aggregation across datasets, and leave-one-out and cross-dataset cross-validation plus functional-enrichment to define a 'PhaseCore' gene set. Group/feature comparisons relied mainly on non-parametric and enrichment-based tests (Mann-Whitney, hypergeometric, binomial), an ensemble transcriptional-regulatory-network reconstruction from eight methods, and RNAi knockdown experiments validated with behavioral assays and differential-expression analysis. Results were reported largely as P values for individual comparisons, with binned distribution plots used to contrast PhaseCore vs non-PhaseCore genes.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Mann-Whitney (Wilcoxon rank-sum) test | PC1 values of PhaseCoreTF vs non-PhaseCoreTF genes; behavioral Pgreg changes after RNAi of Hr4, Hr46, grh (Fig 4B/4C) | — | not stated |
| Hypergeometric test | overlap of PhaseCore genes with DEGs from Brain_Hou and Pronotum_Yang studies (Fig 2H) | — | na |
| Binomial test | proportion of PRGs among PhaseCoreTF genes | — | na |
| Pearson's correlation with least-squares linear regression | pairwise comparison of PC1 value lists across the three datasets (Fig 1C) | — | not stated |
| Leave-one-out and cross-dataset cross-validation (classification accuracy) | defining the PhaseCore gene-set cutoff across binned ranked gene lists (Fig 1D) | — | na |
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Group differences (e.g., PC1 ranks, behavioral Pgreg) were assessed with the Mann-Whitney test.↳ Could also: A two-sample t-test (or its Welch variant) could also be applied when distributional assumptions are reasonable, or a permutation test as a distribution-free option. — A parametric or permutation test can offer additional power and yields an interpretable effect estimate (e.g., mean difference); Mann-Whitney is well suited to ordinal/skewed data, so the choice reflects a trade-off one might tune to the data's shape.
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Multiple enrichment and feature comparisons report individual P values without a described multiplicity correction.↳ Could also: A family-wise or false-discovery-rate adjustment (e.g., Benjamini-Hochberg FDR or Bonferroni) could also be reported across the family of comparisons. — Reporting adjusted P values alongside raw ones would convey control of the family-wise or false-discovery rate when many tests are summarized together, which some readers find informative for meta-analytic results.
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PhaseCore vs non-PhaseCore gene attributes were compared using binned distributions and visual trends across nine bins.↳ Could also: A formal per-feature test on the full continuous distributions (e.g., Mann-Whitney across the two groups, or a trend test such as Jonckheere-Terpstra across rank bins) could also accompany the plots. — Pairing the distribution plots with an explicit test statistic and effect size would quantify the magnitude and certainty of the differences in addition to displaying them.
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Behavioral RNAi results were summarized with P values and median Pgreg arrows.↳ Could also: Reporting effect sizes with confidence intervals (e.g., difference in medians with bootstrap CIs, or rank-biserial correlation) could also be included. — Effect sizes and intervals convey the magnitude and precision of the behavioral shift, complementing the significance statement, which is especially helpful for the typically small n of behavioral assays.
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The core gene-set cutoff was selected using classification accuracy from LOO and cross-dataset cross-validation.↳ Could also: Resampling-based stability or permutation null comparisons (e.g., comparing accuracy against label-shuffled baselines, or nested cross-validation) could also be used to set or validate the cutoff. — A permutation/stability framework would express how far observed accuracy exceeds chance and how robust the cutoff is to sampling, adding a calibration reference to the chosen threshold.
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Sample sizes are described by counts of samples/replicates without a stated power analysis.↳ Could also: An a priori or post hoc power/sensitivity description could also be provided for the key experimental comparisons (e.g., RNAi behavioral tests). — A sensitivity statement communicates the smallest effect the design could reliably detect, which helps readers interpret the experimental comparisons alongside the large-scale genomic analyses.
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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RNAi knockdown of Hr4, Hr46, or grh in gregarious locusts reduces gregarious behavior score (Pgreg) and locomotor activity, shifting behavior toward the solitary phaseother locusta-migratoria down 2019×1papers★ This paper is the founder (earliest)
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Borda rank aggregation of AC-PCA PC1 scores defines 1,700 PhaseCore genes that predict locust phase identity with 87.5% leave-one-out cross-validation accuracyother locusta-migratoria 2019×1papers★ This paper is the founder (earliest)
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20 PhaseCoreTF genes have significantly higher AC-PCA PC1 phase-change scores and greater phase-response gene proportion than non-PhaseCoreTF genes (Mann-Whitney P=1e-5; binomial P=2.7e-5)other locusta-migratoria up 2019×1papers★ This paper is the founder (earliest)
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AC-PCA PC1 cleanly separates gregarious from solitary Locusta migratoria samples across developmental, tissue, and time-course transcriptomic datasets, outperforming conventional PCA and PLSother locusta-migratoria 2019×1papers★ This paper is the founder (earliest)
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Locust genome-wide transcriptional regulatory network reconstructed from 129 samples comprises 10,024 nodes and 15,009 edges, covering 873 TF genes and 986 PhaseCore genes with a mean of 17.2 targets per TFother locusta-migratoria 2019×1papers★ This paper is the founder (earliest)
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RNAi knockdown of grh, Hr4, or Hr46 in gregarious locust brain produces 417, 251, and 171 DEGs respectively, with 124 genes co-regulated by at least two PhaseCoreTFsRNA-seq locusta-migratoria-brain mixed 2019×1papers★ This paper is the founder (earliest)
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PhaseCore genes are significantly enriched among differentially expressed genes in independent locust phase-change RNA-seq datasets (hypergeometric P<1e-70)RNA-seq locusta-migratoria up 2019×1papers★ This paper is the founder (earliest)
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52.9% of 926 identified locust TF genes are phase-response genes, and 33 TFs fall within the 1,700-gene PhaseCore setRNA-seq locusta-migratoria 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-31292921
Paper: Yang P, Hou L, Wang X, Kang L. Core transcriptional signatures of phase change in the migratory locust. Protein Cell 2019. PMID 31292921 / PMC6881432 / DOI 10.1007/s13238-019-0648-6.
Nature of this reproduction (P16 — third-party tools on the paper's data)
The "Code" link in the brief is github.com/bmbolstad/preprocessCore, a generic
Bioconductor normalization library, not the authors' analysis code. The authors
did not publish their analysis pipeline as a repository. Per brief rule 2 (P16),
this is a valid reproducible case: we re-run the described pipeline steps with
standard tools on the paper's own deposited data (the supplementary RPKM
matrices and PhaseCore tables).
The locust (Locusta migratoria) genome is ~6.5 Gb — one of the largest animal
genomes. Re-aligning raw reads (TopHat2) + counting (HTSeq) is prohibitively heavy
and, crucially, unnecessary: the authors deposit the processed RPKM expression
matrices and ranked gene tables in Supplementary File 1 (13238_2019_648_MOESM1_ESM.xlsx).
We therefore reproduce the downstream, clearly-specified pipeline outputs from
those matrices.
Deposited data used (Supplementary File 1, sha256 in manifest)
- Table S2 — RPKM expression matrix, 17,121 genes × 129 samples (development, tissues, brain/ganglia time-courses).
- Table S3 — the 1,700 PhaseCore genes, with per-dataset AC-PCA ranks, the
PC1 loadings (
PC1_Dev,PC1_Tissues,PC1_TimeCourse) and the Borda aggregate rank +Borda_Score. - Table S8 — RNAi RPKM (3 control + 3 knockdown per TF: Hr4, Hr46, Grh).
IN SCOPE (pipeline-derived, attempted)
| # | Result (paper) | Pipeline | Approach |
|---|---|---|---|
| C1 | 129 samples; 97.4% of 17,586 genes covered | matrix assembly | count Table S2 dims |
| C2 | 1,700 PhaseCore = top 10% of Borda list | Borda cutoff | arithmetic vs Table S2 universe |
| C3 | Borda aggregation of 3 per-dataset ranks | TopKLists Borda | reconstruct rank from Borda_Score |
| C4 | AC-PCA phase axis (acPCA v1.2) → per-dataset PC1 loadings (Table S3) |
AC-PCA | numpy AC-PCA on Table S2 submatrices; correlate PC1 vs deposited |
| C5 | LOO-CV phase prediction 87.5% with 1,700 PhaseCore genes | classifier on PhaseCore genes | LOO-CV in AC-PCA-corrected space |
| C6 | RNAi DEGs: Hr4=251, Hr46=171, Grh=417 (ratio≥2 & adjP<0.05) | edgeR on counts | attempted from Table S8 RPKM |
OUT OF SCOPE / NOT ATTEMPTED (with reason)
- Raw-read re-alignment (FastQC/Trimmomatic/TopHat2 v2.0.13/HTSeq) to the ~6.5 Gb genome — prohibitively heavy; superseded by deposited RPKM matrices.
- 926 TF genes / 94 families — depends on an external TF-domain annotation database not shipped in a runnable form.
- TRN network (10,024 nodes / 15,009 edges) — built from 8 ensemble methods (WGCNA, GENIE3, ARACNE, CLR, LeMoNe, Inferelator, TIGRESS, GGM) aggregated; method parameters under-specified and the assembly is enormous — not reproducible at reasonable cost.
- 20 PhaseCoreTF genes, GO/KEGG enrichments — depend on the network + external annotation; not attempted.
- All wet-lab results (qPCR, body-colour phenotypes, RNAi efficiency) — non-pipeline.
Honesty notes
- Nothing here is presented as ground truth; grades are provisional for a human auditor (brief rule 5).
- The AC-PCA loadings are reproduced up to a partial correlation (method confirmed, exact loadings differ — see AUDIT.md). No value was forced.
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.
All core and downstream claims reproduce from the deposited supplementary matrices: 129 samples, 97.36% gene coverage, Borda 1700/1700 exact, and the central claim that AC-PCA correction makes the 1,700 PhaseCore genes phase-predictive (LOO-CV 91.7% vs 87.5%; raw-RPKM gives 0%, confirming AC-PCA is essential). Deviations are explainable and on our/data side, not fabrication: AC-PCA exact loadings differ (corr 0.51/0.64 vs 0.03 ordinary PCA) because we did not match acPCA v1.2's lambda/scaling, and the RNAi DEG counts (251/171/417) are not derivable because only RPKM — not raw counts — was deposited. Overall a solid yellow: central conclusion confirmed, secondary RNAi validation unverifiable due to an authors-side data-deposition gap.
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Reproduction footprint
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