De novo transcriptome assembly and comprehensive assessment provide insight into fruiting body formation of Sparassis latifolia.
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 result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
- 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
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
Re-run after requeue (prior pass had completed all control-plane metadata but compute had not executed). This pass EXECUTED the named code artifact on «our HPC»: CD-HIT 4.8.1 (cd-hit-est, exact version) @95% identity on the paper's own deposited data (SLURM «job», n113, 00:02:54). Central finding, now confirmed by direct download + inspection (not just metadata): the public deposit PRJNA799466 contains 219,103 BGI Unigene-format ASSEMBLED TRANSCRIPT CONTIGS (names CL<n>.Contig<n>, synthetic placeholder qualities, transcript-scale lengths), NOT the >60 Gb / 4.8e8 PE150 raw RNA-Seq reads the Methods describe. Running CD-HIT @95% on the 219,103 pooled contigs yields 57,459 non-redundant clusters (24,447 singletons; mean 1,990 bp; N50 2,984 bp) -- the SAME ORDER OF MAGNITUDE as the paper's reported 48,549 unigenes / 2,488 bp / 3,629 bp, strongly suggesting the deposit IS the per-sample assembled transcripts mislabeled as raw reads. VERDICT: the named tool reproduces cleanly at exact version+parameter (C-CDHIT exact); the data-volume/read-count/layout/deposit-type claims are confirmed MISMATCHES (fabrication-relevant: raw reads not in the deposit); the exact assembly statistics are PARTIAL (same scale, not exactly reproducible without the absent raw reads); annotation (C-ANN-) and DEG (C-DEG-) counts were NOT attempted (honest blocker: raw reads + large external DBs). No value was fabricated. A human reviewer must sign off on the deposit-type mismatch and the provisional grades.
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 36assessed: 2026-06-19 ⛓ 4fa687ca31f9
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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
- 2026-06-26
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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: sonnetThe genes and pathways underlying fruiting body formation in Sparassis latifolia are unknown, and comparative transcriptome analysis across the hyphal knot, primordium, and primordium differentiation stages can identify the candidate genes/pathways driving this developmental transition.
- ★ De novo transcriptome assembly of S. latifolia produced 48,549 unigenes, 71.53% (34,728) of which were annotated against KEGG, GO, and/or KOG databases resource
- ★ Primordium initiation (SM to SP transition) is significantly associated with 40-66 KEGG pathways, including ribosome, metabolism of xenobiotics by cytochrome P450, and glutathione metabolism finding
- ★ The MAPK and mTOR signal transduction pathways underwent significant adjustments during the SM to SP transition mechanism
- ★ The PI3K-Akt signaling pathway, related to cell proliferation, plays crucial functions during the SP to SPD developmental transition mechanism
- qRT-PCR validation of 27 genes (fruiting body formation, MAPK, PI3K-Akt, and mTOR pathway genes) matched RNA-Seq expression patterns, confirming data quality method
- eln2-like genes (83 identified) show functional diversification, with most up-regulated at the SP stage and some at the SM stage finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-Seq (de novo transcriptome assembly) | Sparassis latifolia (strain CCMJ 1100) fruiting body tissue: hyphal knot (SM), primordium (SP), primordium differentiation (SPD) | none (developmental stage comparison) | unigene expression (FPKM), DEGs between stages | Illumina Hi-Seq 2500, PE150 |
| transcriptome assembly and clustering | S. latifolia unigenes | none | assembled unigene number, length, N50 | Trinity; CD-HIT |
| sequence homology annotation (BLASTP) | S. latifolia unigenes | none | functional annotation against NR, Swiss-Prot, KOG, KEGG databases | Transdecoder; BLASTP (E-value<1e-5) |
| GO annotation and enrichment | S. latifolia unigenes | none | GO term classification and enrichment of DEGs | Blast2GO; Goatools |
| KEGG pathway enrichment | S. latifolia unigenes/DEGs | none | pathway mapping and enrichment (FDR<0.01) | KOBAS |
| differential expression analysis | S. latifolia, SM vs SP vs SPD stages | developmental stage comparison | DEG counts, up/down-regulated genes (|log2FC|>=1, probability>=0.8) | RSEM; NOIseq |
| qRT-PCR | S. latifolia, SM/SP/SPD samples | none | relative expression of 27 selected genes (18sRNA as internal control) | SYBR Premix ExTaq kit; FAST 7500 (Agilent) |
- – Transcriptome assembly generated 48,549 unigenes with 71.53% (34,728) annotated by KEGG/GO/KOG 48,549 unigenes; 71.53%
- – 8,822 DEGs identified between SM and SP stages 5,195 up-regulated, 3,627 down-regulated
- – 1,347 DEGs identified between SP and SPD stages 690 up-regulated, 657 down-regulated
- ▲ SM-to-SP DEGs significantly enriched in KEGG pathways including ribosome, systemic lupus erythematosus, metabolism of xenobiotics by cytochrome P450, ubiquitin mediated proteolysis 40 pathways, FDR<0.01
- – SP-to-SPD DEGs enriched in pathways including complement and coagulation cascades, glutathione metabolism, tyrosine metabolism 44 pathways, FDR<0.01
- ▲ MAPK pathway genes (Pkc, Mkk1_2, Pbs2, and others) up-regulated during SM to SP transition
- ▲ PI3K-Akt and mTOR signaling pathway genes (PDKs, mTOR, SGKs) up-regulated during SP to SPD development
- – qRT-PCR expression of 27 genes matched RNA-Seq results
- count 48,549 unigenes (total assembled unigenes across all stages)
- count 34,728 (71.53%) (unigenes annotated by KEGG, GO, or KOG)
- count 8,822 DEGs (5,195 up, 3,627 down) (DEGs between SM and SP stages)
- count 1,347 DEGs (690 up, 657 down) (DEGs between SP and SPD stages)
- fold_change |log2(fold change)| ≥ 1, probability ≥ 0.8 (cutoff criteria for DEG designation)
- pvalue Bonferroni corrected P-value 0.01; FDR < 0.01 (significance threshold for GO/KEGG enrichment)
- count 83 eln2-like transcripts (identified via BLAST, E-value < 1e-5, >60% sequence identity)
- count 32,765 unigenes mapped to 297 KEGG pathways; 19,408 unigenes into 25 KOG categories (functional annotation coverage)
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 study used a de novo RNA-Seq transcriptome assembly (Trinity, CD-HIT) across three developmental stages of Sparassis latifolia (SM, SP, SPD), each with three biological replicates. Differentially expressed genes (DEGs) between adjacent stages were identified with the RSEM/NOIseq pipeline using a |log2(fold change)| ≥ 1 and probability ≥ 0.8 cutoff, and functional enrichment (GO/KEGG) was assessed against the whole-transcriptome background with a Bonferroni-corrected P-value threshold of 0.01 (KEGG pathway results are also described using an FDR < 0.01 threshold). A subset of 27 DEGs was validated by qRT-PCR (2^-ΔΔCt method, three biological and three technical replicates) and reported as showing good qualitative agreement with RNA-Seq results.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| NOIseq (non-parametric differential expression method), fold-change/probability cutoff (|log2FC| ≥ 1, probability ≥ 0.8) | Identification of DEGs between SM vs SP and SP vs SPD | 3 biological replicates per stage | not stated |
| GO term enrichment (Goatools) and KEGG pathway enrichment (KOBAS) with Bonferroni-corrected P-value / FDR threshold | Functional enrichment of DEG sets between developmental stages | — | not stated |
| Hierarchical clustering using Pearson's correlation with average linkage | Clustering of sample replicates based on FPKM values (Fig. 4) | 9 libraries (3 stages × 3 replicates) | not stated |
| 2^-ΔΔCt relative quantification (qRT-PCR) | Validation of 27 selected DEGs | 3 biological replicates, 3 technical replicates each | na |
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DEGs were called using the non-parametric NOIseq method with a fold-change and probability (≥0.8) cutoff rather than a model-based test with an adjusted p-value/FDR threshold.↳ Could also: Count-based methods such as DESeq2 or edgeR, which model read-count dispersion and report Wald or likelihood-ratio test p-values adjusted by FDR — These tools are widely used for RNA-Seq designs with biological replicates and provide a formal hypothesis-testing framework alongside fold-change estimates, which some readers find complementary to probability-based approaches like NOIseq.
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GO/KEGG enrichment used a Bonferroni-corrected P-value threshold of 0.01 (with FDR also referenced for KEGG results).↳ Could also: The Benjamini-Hochberg FDR procedure applied uniformly across both GO and KEGG analyses — FDR control is less conservative than Bonferroni and is commonly favored in large-scale genomic enrichment analyses to balance discovery power with false-positive control, and using one consistent method throughout could simplify interpretation.
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qRT-PCR validation of 27 DEGs was described qualitatively as showing good agreement with RNA-Seq, without a stated formal statistical comparison.↳ Could also: A paired statistical test (e.g., a two-tailed t-test or Pearson/Spearman correlation) comparing RNA-Seq log2 fold changes to qRT-PCR ΔΔCt-derived fold changes — A quantitative correlation or significance test can provide an explicit, reproducible measure of concordance between the two platforms beyond a qualitative description.
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Sample clustering was assessed using Pearson's correlation with average-linkage hierarchical clustering of FPKM values.↳ Could also: Principal component analysis (PCA) or Spearman rank correlation as complementary or alternative approaches to visualize replicate similarity — PCA can reveal overall variance structure across samples, and Spearman correlation is robust to non-linear relationships in expression data, both of which are commonly used alongside or instead of Pearson-based hierarchical clustering.
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Dispersion measures (e.g., SD, SEM, or confidence intervals) for qRT-PCR relative expression values are not described in the main text.↳ Could also: Reporting SD or 95% confidence intervals alongside mean relative expression values — Including a measure of variability helps convey the precision of biological replicate measurements and is standard practice when presenting qRT-PCR validation data.
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DEG calling and enrichment were based on three biological replicates per stage.↳ Could also: A stated power or sample-size justification, or inclusion of additional replicates where feasible — Explicitly describing the statistical power afforded by the chosen replicate number can help readers gauge sensitivity to detect true expression differences, particularly for lower-abundance transcripts.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35773379
Paper: Shu L, Wang M, Xu H, Qiu Z, Li T. De novo transcriptome assembly and comprehensive assessment provide insight into fruiting body formation of Sparassis latifolia. Sci Rep 2022. DOI 10.1038/s41598-022-15382-5.
Named code artifact: https://github.com/weizhongli/cdhit (CD-HIT, a third-party clustering tool — used by the paper for redundancy reduction). Per P16, applying this third-party tool to the paper's own data is a fully valid reproduction target.
Named data: SRA BioProject PRJNA799466 (SRP356230).
Pipeline-derived (in-scope) results
| id | result | pipeline / tool | reproducible? |
|---|---|---|---|
| C-DATA | "more than 60 Gb of clean data", "4.8 × 10⁸ RNA-Seq reads", PE150, 9 libraries (3 stages SM/SP/SPD × 3 reps) | sequencing deposit (SRA) | checkable from SRA metadata |
| C-ASM-N | 48,549 unigenes assembled | Trinity (r2014-04-13) + CD-HIT 4.8.1 @95% | needs raw reads |
| C-ASM-MEAN | mean unigene length 2,488 bp | Trinity + CD-HIT | needs raw reads |
| C-ASM-N50 | N50 = 3,629 bp | Trinity + CD-HIT | needs raw reads |
| C-CDHIT | CD-HIT 4.8.1 @95% identity clustering for redundancy reduction | CD-HIT (named repo) | needs assembly input |
| C-ANN-NR | 16.08% of unigenes no Nr hit (~84% annotated) | BLASTP vs NCBI Nr | needs assembly + big DB |
| C-ANN-KOG | 19,408 unigenes (46.6%) into 25 KOG groups | KOG/RPS-BLAST | needs assembly + DB |
| C-ANN-GO | 23,226 unigenes into 58 GO groups | GO mapping | needs assembly |
| C-ANN-KEGG | 32,765 unigenes → 297 KEGG pathways | KEGG (KAAS/KOBAS) | needs assembly |
| C-ANN-ALL | 71.53% (34,728) annotated by ≥1 of KEGG/GO/KOG | annotation union | needs assembly |
| C-DEG-1 | SM→SP: 8,822 DEGs (5,195 up / 3,627 down); cutoff |log2FC|≥1 & prob≥0.8 | align (back to unigenes) + DE | needs raw reads |
| C-DEG-2 | SP→SPD: 1,347 DEGs (690 up / 657 down) | align + DE | needs raw reads |
Out of scope
- Wet-lab: RNA extraction, library prep, qRT-PCR validation, cultivation/morphology.
Critical blocker discovered during dataset profiling (control-plane, no compute)
The paper states raw RNA-seq reads were deposited (>60 Gb, 4.8×10⁸ reads, PE150, 9 paired-end libraries). The actual public deposit PRJNA799466 (confirmed independently on both NCBI SRA runinfo and EBI ENA) contains:
- 9 runs (SRR17701608–SRR17701616), one per library — sample count matches.
- LibraryLayout = SINGLE (paper says paired-end).
- read/spot count ≈ 23.4k–25.2k per run; 219,103 total (paper says 4.8×10⁸).
- avgLength ≈ 1,372–2,097 bp per sequence (paper says PE150 = 150 bp reads).
- total bases = 394,260,982 ≈ 0.394 Gb (paper says >60 Gb) — ~150× short.
The deposited sequences are transcript-length (≈1.4–2.1 kb), single-end, ~24k per sample — consistent with per-sample assembled transcripts/contigs, NOT the raw Illumina reads. Therefore the central de novo assembly pipeline (Trinity → CD-HIT → 48,549 unigenes / N50 / annotation / DEGs) cannot be reproduced from the public data: its required input (raw reads) is absent from the repository.
Revised reproduction plan
- C-DATA (run-count / data-volume claim): reproducible as a mismatch finding — N libraries match (9) but data volume, read count, layout, and read length do NOT match the paper. This is the auditable, fabrication-relevant result.
- C-CDHIT (named tool on deposited data): download the 9 deposited sequence sets to «infra», pool them, and run CD-HIT 4.8.1 @95% (exactly the named tool + parameter) to report the resulting non-redundant cluster count. This exercises the named repository on the paper's own data and yields a real, reportable number, while honestly noting the input is the deposited contigs, not raw reads.
- Assembly/DEG/annotation numbers (C-ASM*, C-ANN*, C-DEG*): not attempted — honest blocker (raw reads absent). Recorded, not fabricated.
Assessments & scoring basis
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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.