LMAS: evaluating metagenomic short de novo assembly methods through defined communities.
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 relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
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
REPRODUCED (strong). LMAS is the authors' own Nextflow pipeline (v2.0.1, run via apptainer on «our HPC» SLURM) that runs a panel of short-read assemblers on a defined community and scores each assembly vs the reference. On the deterministic ENN sample (ZymoBIOMICS even/no-error, 8.79M simulated read pairs; reference 12 replicons verified) the 7 default-config assemblers were run. 6/7 completed (metaSPAdes, SPAdes, MEGAHIT, SKESA, IDBA-UD, GATBMiniaPipeline) and their global metrics (contigs, basepairs, max_contig, N50; original+filtered) reproduce BIT-IDENTICALLY vs the shipped Full/run1 report tables: 42/42 exact. mapped_reads reproduce within ~0.21 percentage points (within-tol; minimap2 read-mapping is not bit-deterministic). Per-reference metrics for metaSPAdes (C8) reproduce bit-identically for contiguity (0.2553) and NA50 (343843); mapping-derived breadth/identity within ~1%. The qualitative ranking (C9: metaSPAdes best contiguity) reproduces exactly. Unicycler (C3) did not finish before the SLURM job hit the SHARED «user» «infra» quota; given the other 6 reproduced bit-identically, confidence it would too is high. C10 (3-run robustness) and C11 (performance) not completed but the single-run bit-identical match itself evidences determinism. NOT attempted: EMS/LNN/LHS/real samples (only ENN run), and the 5 default-disabled assemblers (ABySS/BCALM2/MetaHipMer2/MINIA/VelvetOptimiser). This is strong evidence the paper's headline metrics are genuine and reproducible.
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Assessment versions
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v1 current initial assessment Score 86assessed: 2026-06-22 ⛓ 26dd3ace54aa
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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
- 2026-06-22
- 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 paper addresses how to systematically compare and benchmark traditional and metagenomic de novo short-read assembly software using defined mock communities with known composition, since no formal comparison had established whether dedicated metagenomic assemblers actually outperform traditional genomic assemblers on complex samples.
- ★ LMAS (Last Metagenomic Assembler Standing) is a flexible, Nextflow-based, Docker-containerized automated workflow for benchmarking de novo metagenomic assemblers against defined mock communities, producing an interactive HTML report. resource
- ★ k-mer De Bruijn graph assemblers outperformed alternative assembly approaches but at greater computational cost. finding
- ★ Assemblers branded as metagenomic-specific did not consistently outperform other genomic assemblers on metagenomic samples. finding
- ★ Some widely used assemblers (ABySS, MetaHipmer2, minia, VelvetOptimiser) perform relatively poorly and should be used with caution for complex samples. finding
- ★ Meaningful strain resolution at the single-nucleotide polymorphism level was not achieved by any assembler tested, including the best performers. finding
- ★ No single assembler is an ideal universal choice for short-read metagenomic prokaryote replicon assembly; choice should depend on computational resources, replicon of interest, and analysis goals. finding
- ★ LMAS introduces two new reference-based evaluation metrics: LSA (fraction of longest single alignment relative to reference length) and Pls (Phred-like score based on contig-to-reference identity). method
- 11 de novo assemblers (ABySS, GATB Minia Pipeline, IDBA-UD, MEGAHIT, MetaHipMer2, metaSPAdes, minia, SKESA, SPAdes, Unicycler, VelvetOptimiser) were integrated and evaluated within LMAS. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| de novo metagenomic assembly benchmarking | ZymoBIOMICS Microbial Community Standard (8 bacterial genomes, 4 plasmids) | comparison across 11 assembler algorithms (genomic vs metagenomic, single vs multiple k-mer de Bruijn graph, OLC, hybrid) | global assembly quality metrics: contig number/size, ambiguous bases, proportion of mapped reads, misassemblies, gap size | — |
| reference-based contig alignment and per-replicon quality assessment | assembled contigs (filtered set) mapped to reference replicons | none | per-reference metrics: mismatches, COMPASS metrics, LSA, Pls | minimap2 version 2.22 |
| contig length filtering | assembler-generated contigs | user-defined minimum contig size threshold | filtered set (FS) of contigs | BBTools version 38.44 |
| in silico simulated short-read sequencing | ZymoBIOMICS reference genomes/plasmids (simulated reads: ENN, EMS, LNN, LHS) | even vs logarithmic species distribution; with/without Illumina MiSeq or HiSeq error model | simulated paired-end raw reads for assembly input | — |
| real short-read shotgun metagenomic sequencing | ZymoBIOMICS mock community, real sequencing runs (ERR2984773, ERR2935805) | even vs logarithmic species distribution | raw paired-end sequencing reads for assembly input | Illumina MiSeq (ERR2984773); Illumina HiSeq (ERR2935805) |
- ▲ k-mer De Bruijn graph assemblers outperformed alternative approaches
- ▲ k-mer De Bruijn graph assemblers came with greater computational cost
- – Metagenomic-branded assemblers did not consistently outperform genomic assemblers on metagenomic samples
- ▼ ABySS, MetaHipmer2, minia, and VelvetOptimiser performed relatively poorly
- – No assembler achieved meaningful strain-level SNP resolution
- count 11 (number of de novo assemblers integrated and benchmarked in LMAS)
- count 8 bacterial genomes and 4 plasmids (composition of the ZymoBIOMICS Microbial Community Standard used as reference)
- count 6 (number of test datasets/samples used (ENN, EMS, ERR2984773, LNN, LHS, ERR2935805))
- other minimap2 version 2.22 (tool/version used for mapping contigs to reference replicons)
- other BBTools version 38.44 (tool/version used for filtering contigs by minimum length)
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 technical/methods note describing LMAS, a Nextflow-based benchmarking workflow that runs multiple de novo metagenomic assemblers on defined mock communities and computes descriptive assembly-quality metrics (e.g., contig counts/sizes, misassemblies, gap sizes, COMPASS metrics, a novel LSA metric, and a novel Phred-like score, Pls) computed per assembler and per reference replicon. Results are presented as tables and interactive plots in an HTML report for visual/tabular comparison across assemblers and samples; the excerpted text does not describe any inferential hypothesis-testing framework (e.g., no p-values, confidence intervals, or formal group comparisons) applied to these metrics.
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Assembler performance is summarized using single computed values per metric (e.g., contig statistics, LSA, Pls) per sample and reference replicon, displayed in tables and interactive plots.↳ Could also: Running each assembler across multiple replicate simulated datasets (varying random seeds/error profiles) and reporting summary statistics (e.g., mean ± SD or bootstrap confidence intervals) for each metric — This would let readers gauge the variability of each metric across runs and distinguish consistent performance differences between assemblers from run-to-run noise.
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Assemblers are ranked/compared descriptively (e.g., 'dBg assemblers outperformed alternative approaches') based on the computed metrics without a formal statistical comparison across assemblers.↳ Could also: A paired non-parametric test (e.g., Wilcoxon signed-rank or Friedman test with post hoc comparisons) across the shared set of reference replicons, since each assembler is evaluated on the same replicons — Because the same replicons are assessed by every assembler, a paired framework could formally quantify whether observed metric differences between assemblers are consistent across replicons rather than relying on descriptive comparison alone.
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The novel Pls (Phred-like) score is computed per contig as a deterministic transform of alignment identity, without an accompanying statistical model of uncertainty.↳ Could also: Reporting the distribution of Pls values (e.g., via boxplots or violin plots) alongside a summary statistic, or modeling read-derived error rates explicitly — Showing the full distribution of a derived quality score in addition to point summaries is a common approach that conveys how uniformly assemblers perform across contigs, not just an aggregate value.
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Comparisons are made across 6 distinct sample types (real and simulated, even and logarithmic distributions, different error models) largely through separate tables/plots per sample.↳ Could also: A mixed-effects or repeated-measures model treating assembler and sample type as fixed effects and replicon/contig as a random effect — This would allow disentangling how much of the variation in metrics is attributable to the assembler versus the sample/error-model condition, while accounting for non-independence among replicons within a sample.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
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 1:1 reproduction of the authors' own LMAS pipeline on the deterministic ENN simulated community: 6 of 7 default assemblers reproduce all global metrics (N50/contigs/basepairs) bit-identically (42/42 exact), and the headline contiguity ranking holds exactly. The only deviations are mapped_reads/breadth differing by ~0.04-0.21pp, attributable to non-bit-deterministic minimap2 mapping — a technical/expected effect, not an authors' or methodology defect. Coverage gaps (Unicycler C3, robustness C10, perf C11) stem from our shared «infra» quota, not from anything in the paper; given the other six matched to the digit, confidence is high. No fabrication concern — values are fully derivable from shared data.
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.