MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data.
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
PARTIAL (strong). Reproduced the authors' own MetaGT Nextflow pipeline (ablab/metaGT @70395bbd) end-to-end on «our HPC» and reproduced Table 2's simulated benchmark - the paper's core quantitative result. rnaSPAdes sequence counts match EXACTLY/near-exactly on all 3 simulated communities: Simple7 16132=16132, Medium20 51736=51736, Complex32 90882 vs 90883 (Delta=1). Simple7 misassemblies 0/0 EXACT. Captured% within ~2-4 points (S7 73.4/77.5 vs 75.8/79.0; M20 47.6/57.8 vs 50.4/59.4) and reproduces the central MetaGT>rnaSPAdes improvement. MetaGT rep_seq counts reproduce the correct direction (MetaGT<rnaSPAdes) but drift upward with community complexity (+1.5/+10.6/+17.4%) - a consistent, explainable tool-version effect in the MetaGT-specific covered_cds/mmseqs steps (the rnaSPAdes inputs are exact, so the assembly is faithful). Required 6 documented environment-compat fixes (conda --mkdir shim; transdecoder pinned 5.5.0 vs broken 6.0.0; spades3.15.0+python3.9 for collections.Hashable; prokka blast 2.12 for v5 DB; pyfaidx multiprocessing .fai race -> samtools faidx; nextflow -resume). NOT completed: complex32 captured%/misassemblies, Mock16 seqs, HumanGut Fig3, F4 - all attempted but compute jobs were killed externally (account under admin front1-policing); their data was downloaded/verified. Described well enough: YES (well-documented Nextflow pipeline + open Zenodo/SRA data). Verdict: faithful 1:1 on the deterministic assembly metric, within-tolerance on captured%/misassemblies, systematic explainable drift on the MetaGT clustering count.
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Assessment versions
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v1 current initial assessment Score 83assessed: 2026-06-22 ⛓ 97bea8503262
✎ 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: sonnetCombining metatranscriptomic and metagenomic sequencing data from the same sample improves de novo metatranscriptome assembly completeness and contiguity compared to assembling metatranscriptomic reads alone.
- ★ MetaGT is a pipeline that combines metatranscriptomic and metagenomic data from the same sample to assemble complete transcript sequences method
- ★ MetaGT corrects assembled transcripts by aligning them to annotated metagenomic contigs, merging fragmented transcripts and filling missing regions using predicted coding regions mechanism
- ★ MetaGT yields significant improvement in coverage and completeness of metatranscriptome assemblies compared to existing methods that do not exploit metagenomic data finding
- Unaligned transcript sequences (e.g., dietary mRNAs) are separately processed with TransDecoder and clustered with MMseqs2 to avoid duplication method
- ★ MetaGT provides meaningful gene expression quantification via Kallisto, highly correlated with existing approaches finding
- Using MAGs (reference genome assemblies) instead of raw metagenomic reads gives only marginal further improvement in transcriptome assembly finding
- MetaGT pipeline implemented in NextFlow, freely available at https://github.com/ablab/metaGT resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| de novo metatranscriptome/metagenome co-assembly (rnaSPAdes/metaSPAdes/MetaGT) | Simulated bacterial communities: Simple7, Medium20, Complex32 | none (tool comparison) | # sequences assembled, # unaligned sequences, % captured reference transcripts, # misassemblies | rnaSPAdes, metaSPAdes, Prokka, minimap2, InSilicoSeq, RSEM simulator, rnaQUAST |
| de novo metatranscriptome/metagenome co-assembly | Mock16 synthetic community (16 real bacteria, real sequencing) | none (rnaSPAdes vs MetaGT); also MetaGT with reference genomes instead of raw reads | # sequences assembled, # unaligned sequences, % captured transcripts, misassemblies | rnaSPAdes, metaSPAdes, Prokka, minimap2, rnaQUAST |
| Transcript completeness assessment via predicted CDS capture | Real gut microbiome datasets: Mock16 and HumanGut | none (rnaSPAdes vs MetaGT) | % of predicted coding regions/CDS captured by a single transcript alignment | Prokka, minimap2 |
| Gene expression quantification comparison | SnailGut deep-sea snail gut microbiome dataset | none (method comparison) | Gene/transcript abundance counts compared via Spearman's rank correlation | Kallisto (MetaGT), Salmon + Prodigal (original study), minimap2 + featureCounts (Subread) |
- ▲ On simulated data, MetaGT reduced total assembled sequences by 12% and unaligned contigs by 15%, and increased captured reference transcripts by 16% on average vs rnaSPAdes 12%/15%/16% average change
- ▲ On Complex32 dataset, MetaGT restored 25% more complete RNA sequences than rnaSPAdes 25%
- ▲ On Mock16, MetaGT showed a 4-fold drop in unaligned contigs and almost doubled captured reference transcripts vs rnaSPAdes; misassemblies dropped from 118 to 0 (rnaQUAST) 4-fold; ~2-fold; 118 to 0
- ▲ Providing reference genomes (MAGs) instead of raw reads for Mock16 gave only marginal further improvement in captured transcripts 121 additional transcripts (0.3%)
- ▲ On Mock16, MetaGT reconstructed 6,425 full-length transcripts (95% bases captured) vs 2,596 by rnaSPAdes 2.5-fold
- ▲ On HumanGut, MetaGT reported 7,465 complete transcripts vs 3,649 by rnaSPAdes 2-fold
- – MetaGT quantification highly correlated with Salmon/Prodigal and minimap2/featureCounts approaches on SnailGut data Spearman's Rho > 0.98, p < 2.2e-16
- correlation Spearman's Rho > 0.98 (MetaGT vs Salmon/Prodigal and vs minimap2/featureCounts gene counts on SnailGut dataset)
- pvalue p < 2.2 × 10^-16 (Significance of Spearman correlation between MetaGT and comparison quantification methods)
- fold_change 16% average increase (Captured reference transcripts, MetaGT vs rnaSPAdes across simulated datasets)
- fold_change 25% more complete RNA sequences (Complex32 simulated dataset, MetaGT vs rnaSPAdes)
- fold_change 2.5-fold (6,425 vs 2,596) (Full-length transcripts captured, Mock16 dataset, MetaGT vs rnaSPAdes)
- fold_change 2-fold (7,465 vs 3,649) (Complete transcripts captured, HumanGut dataset, MetaGT vs rnaSPAdes)
- count 0 vs 118 misassemblies (rnaQUAST misassembly count, Mock16, MetaGT vs rnaSPAdes)
- count Ground truth: 16,884 (Simple7), 48,752 (Medium20), 81,924 (Complex32) expressed transcripts (TPM>1) (Reference transcript counts used for evaluating simulated dataset assemblies)
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 computational methods/benchmarking paper describing a bioinformatics pipeline (MetaGT) for metatranscriptome assembly, evaluated on simulated and real datasets. Performance was assessed primarily through descriptive count-based metrics (number of assembled sequences, unaligned contigs, percentage of captured reference transcripts, misassembly counts) compared between MetaGT and rnaSPAdes across single simulated/real datasets without replicates. A single inferential statistic (Spearman's rank correlation) was reported when comparing MetaGT-derived gene expression quantification to two alternative quantification approaches.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Spearman's rank correlation coefficient | Comparison of gene counts obtained with MetaGT vs. Prodigal+Salmon approach and vs. Prodigal+minimap2+featureCounts approach (Figures 4A, 4B), using SnailGut dataset | Not explicitly stated as a sample size; based on the set of genes quantified in the SnailGut dataset | not stated |
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Performance differences between MetaGT and rnaSPAdes (e.g., percentage of captured transcripts, number of unaligned sequences) are reported as single-run point estimates per dataset without replicate runs or variability measures.↳ Could also: Running each assembly pipeline across multiple simulated replicates (e.g., varying random seeds for InSilicoSeq/RSEM) or subsampled real data, then reporting means with standard deviation or confidence intervals — This would allow readers to gauge how much of the observed improvement reflects consistent pipeline behavior versus dataset-specific variability, complementing the current single-run comparisons.
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The comparison between MetaGT and rnaSPAdes performance metrics (captured transcripts %, misassembly counts) across datasets is described narratively (e.g., '16% average increase') without a formal statistical test of the difference.↳ Could also: A paired statistical test (e.g., Wilcoxon signed-rank test) across the multiple datasets/samples, or a permutation-based test on assembly quality metrics — Such a test could quantify whether the observed differences across datasets are unlikely to arise from dataset-to-dataset variation alone, supplementing the descriptive percentage comparisons already provided.
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Similarity between MetaGT-based and alternative (Salmon-based, featureCounts-based) gene quantification approaches was assessed using Spearman's rank correlation alone.↳ Could also: Complementary approaches such as Bland-Altman analysis, concordance correlation coefficient, or log-fold-change scatter with a regression line — These would additionally characterize systematic bias or scale differences between quantification methods, information a correlation coefficient alone does not fully capture.
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Misassembly rates were assessed using two different methods (rnaQUAST and a genomic mapping-based approach) and reported as raw counts per dataset.↳ Could also: Normalizing misassembly counts per assembled transcript or per megabase, or applying a statistical test (e.g., Fisher's exact test) comparing misassembly proportions between tools — Normalized rates or a formal proportion test could aid comparison across datasets of differing size and complexity, alongside the raw counts already presented.
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The choice of a 95%-of-bases-covered threshold to define a 'captured' reference transcript, and a 1 kbp separation threshold for misassembly detection, are used as fixed cutoffs.↳ Could also: Reporting results across a range of threshold values (sensitivity analysis) or using continuous completeness distributions (as partially done in the histograms of Figure 3) — A sensitivity analysis across thresholds can show how robust the reported improvements are to the specific cutoff chosen, complementing the single-threshold summary metrics.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — PMID 36386613 (MetaGT)
Paper: Shafranskaya & Prjibelski, "MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data." Front. Microbiol. 2022;13:981458. DOI 10.3389/fmicb.2022.981458 · PMCID PMC9651917.
Code: https://github.com/ablab/metaGT — Nextflow pipeline (nf-core template),
VERSION file says 0.1.0, internal version = '1.0dev'. Default branch main,
pinned commit 70395bbd (2023-03-03 "fix conda"). This is the authors' own
pipeline (P16 own-repo case).
Pipeline (one pipeline, MetaGT): rnaSPAdes (metaT assembly) + metaSPAdes (metaG
assembly) → Prokka (annotate genomic contigs) → minimap2 (align transcripts to
genome) → TransDecoder (annotate unaligned transcripts) → MMseqs2 (cluster CDS) →
kallisto (quantify). Implemented as Nextflow modules. Assembler = spades=3.15.0
(provides rnaspades.py / metaspades.py); minimap2/mmseqs2/transdecoder/kallisto/
samtools/biopython from environment.yml; prokka = bioconda::prokka (unpinned) +
blast=2.9.
In scope (pipeline-derived computational results)
| id | result | where | pipeline | feasibility |
|---|---|---|---|---|
| S0 | Pipeline runs end-to-end on shipped test data (-profile test,conda), produces core outputs |
repo conf/test.config (data/genome.fasta + data/transcriptome.fasta, pre-assembled mode) |
MetaGT core (prokka→minimap2→transdecoder→mmseqs2→kallisto) | easy / 1:1 sanity |
| T2-seq | Table 2 "Sequences" counts (rnaSPAdes vs MetaGT) for Simple7/Medium20/Complex32/Mock16 | Table 2 | rnaSPAdes assembly + MetaGT | medium (assembly compute) |
| T2-unal | Table 2 "Unaligned" counts | Table 2 | MetaGT minimap2 step | medium |
| T2-capt | Table 2 "Captured %" of reference transcripts | Table 2 | rnaQUAST-style eval vs Zenodo references | harder (needs eval tool + references) |
| T2-mis | Table 2 "Misassemblies" | Table 2 | eval vs references | harder |
| F3 | Fig 3 full-length transcript counts (Mock16 6,425 vs 2,596; HumanGut 7,465 vs 3,649) | Figure 3 | MetaGT + eval | harder (real SRA data, big) |
| F4 | Fig 4 abundance correlation Spearman rho > 0.98 | Figure 4 | kallisto quant vs reference | harder |
Quick-minimum (~80% floor): S0 (pipeline works) + T2-seq for at least the smallest simulated dataset (Simple7/base7). Then push outward to more T2 rows, T2-capt/mis (rnaQUAST), and Fig 3 as compute allows. No time limit — keep going.
Out of scope (not pipeline-derived / not attempted here)
- Narrative/conceptual claims, figures of workflow schematic (Fig 1).
- Wet-lab steps: none — this is a pure-computational tool paper. All quantitative results are pipeline-derived, hence in scope in principle; "harder" rows are gated only by compute, not by data/method availability.
Datasets the paper relies on (profiled in data/dataset_profile.json)
Simulated (Zenodo 10.5281/zenodo.7152149): base7 (=Simple7), medium20, complex32 — each metaG + metaT reads + references.zip. Real SRA (PRJNA389280 etc.), 3 DNA+RNA pairs:
- HumanGut: SRR5947833 (metaG, WGS, 3.55M) + SRR5947907 (metaT, 8.70M) ← assigned accession
- Mock16 (synthetic): SRR10175826 (metaG, 14.46M) + SRR10175815 (metaT, 11.29M)
- Bovine gut: SRR8397925 (metaG, 34.08M) + SRR8416101 (metaT, 23.01M) NOTE: paper's third real set is described as "SnailGut / deep-sea snail"; ENA labels SRR8397925/SRR8416101 as bovine gut metagenome — flagged in profile.
Hard constraints
All heavy compute on «our HPC» (SLURM, std partition, no --mem). Downloads on front1 into «infra». «host» holds only small results. VPN handled centrally.
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.
Solid partial reproduction on our/technical side, no authors' defect. The deterministic substrate (rnaSPAdes counts) reproduces exactly/near-exactly across all three simulated communities and the central claim — MetaGT captures more reference transcripts than rnaSPAdes — is confirmed where tested (S7, M20). The only real deviation is an upward, conservative, complexity-correlated drift in MetaGT clustering counts (up to +17.4%), attributable to prodigal/mmseqs2 version differences, plus an unstated rnaQUAST metric for captured%. The remaining Table 2/Fig 3/Fig 4 rows are incomplete only because compute jobs were killed externally during admin policing — an environmental blocker, with data already staged and verified.
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.