Characterization of protein isoform diversity in human umbilical vein endothelial cells via long-read proteogenomics.
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
- ✓The central claim held under reproduction
- 🟡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 and 1:1 reproducible. The paper's own code is a Nextflow long-read-proteogenomics (LRP) pipeline (v1.0.0, MIT, archived) that is ALSO a reusable tool, with md5-verified deposited outputs on Zenodo. Route 1 = independent recomputation of every reported number directly from the deposited per-isoform / per-protein / per-peptide / MetaMorpheus tables (a faithfulness/fabrication check). Result: 13 of 15 pipeline-derived claims reproduce EXACTLY — the full transcript panel C2-C9 (53,863 transcripts / 10,426 genes / 31,668 FSM / 13,746 NIC / 8,449 NNC / 8,522 multi-isoform genes / 2,846 bp), the protein-database panel C10-C11 (34,531 filtered isoforms from 10,912 genes; 71,511-entry hybrid DB split 44,836 GENCODE / 26,675 PacBio), the MS2 spectra count C12 (3,772,771, recomputed by summing the 17 deposited MetaMorpheus fractions), and gene-level peptide evidence C13 (10,444). Two are partial: C14 (deposited 2,755 PacBio isoforms with unique peptides vs paper 2,597, ~6% off; exact definition needs the manuscript-analysis notebook not shipped in the pipeline repo) and C15's secondary count (108 novel peptides EXACT, but '39 at Q<0.001' not derivable from the deposited tables -> 72). One genuine paper-side inconsistency surfaced: C10's pNNC is printed as 12,389 but the deposited data gives 12,380, and 16,296+5,855+12,389=34,540 != the stated total 34,531 (12,380 sums correctly) — a typo, not a fabrication. NOT ATTEMPTED as out-of-scope: wet-lab steps (culture, library prep, sequencing, MS acquisition), the 30 manually-validated novel peptides (human curation), GO enrichment, and UCSC track aesthetics. Overall: a faithful, auditable, 13/15-exact reproduction with no evidence of fabrication.
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 93assessed: 2026-06-22 ⛓ 18c74fb695e2
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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
- 2026-06-22
- 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: sonnetThe study tests whether applying a long-read (PacBio) proteogenomics approach—combining full-length transcript sequencing with mass-spectrometry proteomics—can more accurately characterize the RNA and protein isoform landscape of human umbilical vein endothelial cells (HUVECs) than prior short-read-based methods, including detection of novel isoforms relevant to endothelial function.
- ★ Long-read RNA-seq detected 53,863 transcript isoforms from 10,426 genes in HUVECs, of which 22,195 were novel finding
- ★ The predominant transcript isoform in HUVECs does not match the accepted reference isoform 25% of the time, with vascular pathway-related genes among this group finding
- ★ 2,597 protein isoforms were supported by unique peptides via MS, with 2,280 additional isoforms nominated upon incorporation of long-read transcript evidence finding
- ★ A novel alternative splice acceptor was characterized in the endothelial gene CDH5, suggesting potential changes in associated signalling pathways finding
- ★ Novel protein isoforms arising from diverse RNA splicing mechanisms were identified and supported by uniquely mapped novel peptides finding
- ★ Long-read (PacBio) sequencing provides unambiguous full-length transcript connectivity that short-read RNA-seq cannot, enabling accurate full-length protein isoform prediction mechanism
- ★ A Nextflow-based long-read proteogenomics pipeline integrating PacBio Iso-Seq transcript data with MetaMorpheus-based MS searching was applied to generate a HUVEC sample-specific protein database method
- ★ This represents the first application of long-read proteogenomics to primary endothelial cells resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Long-read RNA-seq (PacBio Iso-Seq) | HUVECs (primary human umbilical vein endothelial cells) | none | full-length transcript isoforms, novel transcript detection, full-length read CPM | PacBio Sequel II, SMRTLink v9 |
| Bottom-up mass spectrometry proteomics (nanoLC-MS/MS) | HUVECs (tryptic digest of cell lysate) | none | peptide/protein identification, protein isoform detection | Orbitrap Eclipse Tribrid MS coupled to Dionex Ultimate 3000 |
| Offline high-pH RP-HPLC fractionation | HUVEC tryptic peptide digest | none | peptide fractions for downstream LC-MS/MS | Agilent 1200 HPLC, Hypersil Gold C18 column |
| Transcript isoform classification (SQANTI3) | HUVEC PacBio-derived transcripts | none | isoform classification vs GENCODE reference (FSM/NIC/NNC etc.) | SQANTI3 v1.3 |
| ORF prediction (CPAT) | HUVEC PacBio transcript isoforms | none | candidate open reading frames, best ORF per transcript | CPAT |
| MS database search (MetaMorpheus) | HUVEC MS spectra searched against HUVEC-specific, GENCODE, and UniProt protein databases | none | peptide spectral matches, peptide and protein groups at 1% FDR | MetaMorpheus v0.0.316 (custom Nextflow branch) |
| Manual novel peptide spectral validation / genome browser mapping | HUVEC novel peptides | none | manual confirmation of novel peptide spectra and isoform mapping | MetaDraw; UCSC Genome Browser |
- – 53,863 transcript isoforms detected from 10,426 genes, including 22,195 novel transcripts 53,863 isoforms; 22,195 novel
- – Predominant HUVEC isoform mismatches the reference isoform in a quarter of cases, including vascular pathway genes 25%
- ▲ 2,597 protein isoforms supported by unique peptides; 2,280 additional isoforms nominated with long-read evidence 2,597 + 2,280 isoforms
- – HUVEC sample-specific protein database contained 71,511 entries from 19,982 genes, with the PacBio-derived subset comprising 26,675 protein isoforms from 7,283 genes 71,511 entries / 26,675 PacBio isoforms
- – Novel alternative splice acceptor identified for CDH5
- – Novel protein isoforms supported by uniquely mapped novel peptides across diverse splicing mechanisms
- count 53,863 transcript isoforms from 10,426 genes (total long-read transcript isoforms detected in HUVECs)
- count 22,195 novel transcripts (subset of detected transcripts classified as novel)
- other 25% (proportion of genes where predominant isoform differs from reference isoform)
- count 2,597 protein isoforms supported by unique peptides (MS-detected protein isoforms)
- count 2,280 additional isoforms nominated (isoforms nominated upon incorporation of long-read transcript evidence)
- count 71,511 entries from 19,982 genes; PacBio subset 26,675 protein isoforms from 7,283 genes (HUVEC sample-specific protein database composition)
- count GENCODE v35: 87,729 protein entries from 19,982 genes; UniProt reviewed human (with isoforms): 42,380 protein entries from 20,292 genes (reference databases used for MS searching)
- other 1% FDR (target-decoy false discovery rate threshold for peptide/protein reporting)
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 is a discovery/characterization proteogenomics study integrating PacBio long-read RNA-seq with bottom-up mass-spectrometry proteomics from a single HUVEC cell population, rather than a study built around inferential hypothesis testing between experimental groups. Confidence in reported peptide/protein/transcript identifications is controlled using a 1% false discovery rate (FDR) via target-decoy database searching (MetaMorpheus) and SQANTI3-based transcript/protein classification, with quantitative transcript abundance summarized as counts-per-million (CPM). Results are reported primarily as counts of detected isoforms, genes, and novel peptides/junctions rather than as p-values or effect-size comparisons between conditions.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Target-decoy FDR filtering (1% threshold) for peptide/protein identification | MetaMorpheus MS database search results (PSM, peptide, and protein group level) | — | not stated |
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Confidence in peptide/protein identifications was controlled using a 1% FDR via classic target-decoy searching in MetaMorpheus.↳ Could also: Posterior error probability (PEP) scoring or machine-learning-based rescoring tools (e.g., Percolator) could also be applied alongside or instead of target-decoy FDR. — These approaches can provide complementary, per-PSM confidence estimates and are commonly used to cross-validate FDR-based filtering in shotgun proteomics workflows.
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The study is based on a single pooled HUVEC sample with technical (quadruplicate digestion) rather than biological replicates.↳ Could also: Including biological replicates (e.g., HUVECs from multiple donors, lots, or passages) would also be a standard design choice. — Biological replication would allow estimation of inter-sample variability and would enable formal statistical comparison (e.g., paired t-tests, ANOVA, or replicate-aware differential expression models) of isoform or peptide abundance across conditions, which is not the aim of the current single-sample characterization.
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Transcript abundance was summarized using full-length read counts per million (CPM).↳ Could also: Other normalization metrics such as TPM (transcripts per million) or spike-in-based normalization could also be used. — These alternative metrics are widely used in long-read transcriptomics and can adjust for differences in transcript length or library composition, which may be a consideration when comparing abundance across multiple samples in future work.
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Novel peptide identifications were validated through stringent filtering criteria and manual spectral inspection (MetaDraw) rather than a fully automated statistical validation step.↳ Could also: A formalized, automated confidence-scoring pipeline (e.g., a dedicated novel-peptide FDR or machine-learning classifier trained on validated vs. decoy spectra) could also be used to complement manual curation. — Automated scoring can scale to larger datasets and provide a reproducible, quantitative confidence metric alongside expert manual review.
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The predominant isoform per gene and reference-isoform discrepancies are reported as counts/percentages (e.g., 25% mismatch with reference) without confidence intervals.↳ Could also: Reporting a confidence interval or exact proportion test (e.g., binomial CI) around such percentages could also be included. — A CI around a proportion conveys the precision of the estimate, which can be informative when the underlying gene/isoform counts are used to generalize beyond the sampled dataset.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36457147 (HUVEC long-read proteogenomics)
Paper: Mehlferber et al. 2022, RNA Biol. PMCID PMC9721438. DOI 10.1080/15476286.2022.2141938 Code: github.com/sheynkman-lab/Long-Read-Proteogenomics (Nextflow, MIT, archived) Pinned ref: v1.0.0 (tag sha 2952b15762ddb3033164ea2fcb3b80ecad488a1e) — the release the paper used. Data: SRA PRJNA832812 (1 run SRR18959149, PacBio Iso-Seq CCS), MS raw on MassIVE MSV000089326, deposited pipeline outputs on Zenodo 10.5281/zenodo.7117445, references on Zenodo 5076056.
Pipeline (Nextflow modules → reported results)
The "Long-Read-Proteogenomics" (LRP) pipeline is the authors' own code AND a reusable tool. Modules present: isoseq3, sqanti3, filter_sqanti, cpat, orf_calling, refine_orf_database, protein_classification, sqanti_protein, make_gencode_database, make_hybrid_database, protein_database_aggregate/filter, protein_inference, peptide_analysis, peptide_novelty_analysis, transcriptome_summary, visualization_track.
IN SCOPE (pipeline-derived, attempted)
- Transcript level (IsoSeq3 + SQANTI3) → C1 CCS read count, C2 53,863 transcripts, C3 10,426 genes, C4 22,195 novel, C5 31,668 FSM, C6 13,746 NIC, C7 8,449 NNC, C8 8,522 multi-isoform genes, C9 avg transcript length.
- ORF calling / protein DB (CPAT + protein_classification + make_hybrid_database) → C10 34,531 predicted protein isoforms (pFSM/pNIC/pNNC), C11 71,511-entry hybrid DB.
- MS search (MetaMorpheus) → C12 3,772,771 MS2 spectra (instrument output, fed to MM), C13 10,444 genes w/ peptide evidence, C14 2,597 isoforms w/ unique peptides, C15 108 novel peptides / 39 at Q<0.001.
Reproduction strategy (two complementary routes, both honest)
- Recompute from deposited pipeline output (Zenodo 7117445, 6.6 GB tar.gz): parse the shipped SQANTI classification + protein/peptide tables and recompute C2–C15 summary counts. Tests whether the paper's reported numbers are faithfully derivable from the deposited output (a fabrication check). Most robust; independent of container engine.
- Re-run IsoSeq3 + SQANTI3 on the public CCS BAM (SRR18959149) via the v1.0.0 pipeline to independently regenerate the transcript classification (C2–C9). Depends on Singularity on «our HPC» (repo uses Docker; sqanti3 container is tagged ':sing'). Heavier; attempted after route 1.
OUT OF SCOPE (wet-lab / manual / external — not attempted)
- Cell culture, RNA extraction, library prep, PacBio sequencing, MS sample prep & acquisition (C12's 3,772,771 spectra is an instrument count, not pipeline-derived — verified at metadata level only).
- Manual spectral validation of the 30 novel peptides (human curation, not a pipeline output).
- GO enrichment biological interpretation; UCSC browser track aesthetics.
- The full MetaMorpheus run (C13–C15) requires the MassIVE raw MS download (large) + Docker MetaMorpheus container; attempted only if «our HPC» time/Singularity permit, else recomputed from deposited MS result tables (route 1).
Container/HPC note
Repo hard-codes docker.enabled=true with gsheynkmanlab/* images. «infra» «our HPC» has no Docker; must run Nextflow with Singularity/Apptainer (singularity.enabled, autoMounts already set). This is the main run-time risk → may surface as env_unresolvable for the live rerun (route 2), in which case route 1 (recompute from deposited output) still delivers the comparison.
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.
This is a strong, faithful reproduction: 13 of 15 pipeline-derived claims — including every headline figure (53,863 transcripts, 10,426 genes, 34,531 protein isoforms, 71,511-entry hybrid DB, 3,772,771 MS2 spectra, 10,444 genes with peptide evidence) — recompute exactly from the authors' md5-verified deposited data, with no fabrication signal. The two partials (C14: 2,597 vs 2,755, ~6%; C15 secondary: 39 vs 72) are explainable deviations rooted in a manuscript-analysis notebook the authors did not deposit, so the exact filter/Q-value definitions are not fully derivable from shared data. One genuine paper-side typo surfaced (C10 pNNC 12,389 doesn't sum to the stated 34,531; 12,380 does). Overall solid with minor, explainable, low-severity deviations on secondary counts; the central conclusion holds.
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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.