Gapless provides combined scaffolding, gap filling, and assembly correction with long reads.
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.
- ✓Same input data as the authors
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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; result = qualitatively 1:1 (figure-only paper, so within-tol/partial, never exact). Ran the authors' own tool Gapless v0.4 on the paper's own E. coli PacBio CLR data (ERR1036235, NCTC12655) at all 5 reported coverage levels (113/57/28/14/7x; observed 112.9x full == paper's 113x), evaluated with QUAST-LG vs the NCTC12655 closed reference (GCA_900635235), on «our HPC» («job»). The initial assembler SH-assembly (not shipped/packaged) was substituted with Flye 2.9.6 — the paper itself benchmarks Flye, and this isolates Gapless's effect. FINDINGS: (C3) Flye assemblies improve monotonically with coverage — reproduced cleanly. (C1) Gapless raises continuity where it matters — fragmented low/mid-coverage assemblies: NGA50 +39%/+60% at 7x/14x, 2->1 complete contig (GF 100%) at 57x — neutral at 28x and slightly worse at 113x where the Flye input is already near-complete. (C2) Misassemblies stay low (<=4) throughout and drop 6->2 at 7x. Central qualitative claims SUPPORTED; graded 'partial' overall due to figure-only comparison, the assembler substitution, and regime-dependence. No fabrication signal — claims are derivable from the shipped tool + public data. NOT attempted: dolphin/human Gb-scale benchmarks (compute cost) and competitor-tool comparisons (out of single-tool scope). Notable repro gotchas now documented: RSII bax.h5 needs bash5tools (bax2bam broken); gapless requires pandas<2 (uses removed DataFrame.append); conda envs must be built by prefix on «infra» (home read-only); git absent on compute nodes (use release tarball).
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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
- 2026-06-30
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-07-15no 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: sonnetBecause scaffolding, gap filling, and assembly correction with long reads are tightly connected tasks, performing them jointly in a single tool should outperform running separate specialized tools successively, which lose the information in reads that supported scaffolding.
- ★ gapless is a new tool that combines assembly correction, scaffolding, and gap filling in one pipeline using PacBio or Oxford Nanopore long reads. method
- ★ Most existing tools perform only one of correction, scaffolding, or gap filling, losing useful read information when run as separate successive programs. finding
- ★ gapless outperforms competing tools (SAMBA, LRScaf+LR_Gapcloser, LRScaf+TGS-GapCloser) in continuity and correctness for most datasets and coverages. finding
- ★ gapless has much lower duplication rates than other gap-filling methods for all datasets except E. coli. finding
- gapless removes nearly all original scaffolds during correction, giving lower scaffold continuity than other gap-filling tools at low and very low coverage. finding
- The racon polishing step in the gapless pipeline decreases completeness at very low coverage by reducing contig mapping rate to the reference. finding
- ★ gapless consists of four Python modules (split, scaffold, extend, finish) run via a bash pipeline using minimap2 alignments. method
- gapless is available as open-source software at https://github.com/schmeing/gapless. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| long-read scaffolding/gap-filling assembly benchmark (QUAST-LG evaluation) | E. coli (SH-assembly initial assembly) | coverage subsampling of PacBio CLR reads (113x, 57x, 28x, 14x, 7x) | NGA50, misassemblies, completeness, duplication ratio | PacBio CLR; QUAST-LG |
| long-read scaffolding/gap-filling assembly benchmark (QUAST-LG evaluation) | dolphin (supernova initial assembly) | coverage subsampling of PacBio CLR reads (86x, 43x, 21x, 11x, 5x) | NGA50, misassemblies, completeness, duplication ratio | PacBio CLR; QUAST-LG |
| long-read scaffolding/gap-filling assembly benchmark (QUAST-LG evaluation) | human (supernova initial assembly) | coverage subsampling of PacBio HiFi reads (33x, 16x, 8x, 4x) | NGA50, misassemblies, completeness, duplication ratio | PacBio HiFi; QUAST-LG |
| long-read scaffolding/gap-filling assembly benchmark (QUAST-LG evaluation) | human (supernova and Flye-from-HiFi initial assemblies) | coverage subsampling of Oxford Nanopore reads (121x, 61x, 30x, 15x, 8x) | NGA50, misassemblies, completeness, duplication ratio | Oxford Nanopore; QUAST-LG |
| read-alignment filter evaluation (false discovery rate / fraction possibilities kept) | four benchmark datasets (E. coli, dolphin, human) at 21x-33x coverage | grid search of filter parameter values (alignment length, quality, distance) | false discovery rate; fraction of true positive bridges retained | minimap2 alignments compared to reference assemblies |
- ▲ gapless outperforms direct competitor gap-filling tools in correctness and continuity for most coverages in the E. coli dataset.
- ▲ gapless achieves higher continuity than other gap fillers for human PacBio HiFi data at full coverage. >3-fold
- ▲ Direct Flye assembly achieves higher contig continuity than gap-filling approaches at full 33x HiFi coverage. >7-fold
- – gapless shows lower duplication rates than other gap-filling methods across datasets except E. coli, where the trend reverses.
- – SAMBA does not make significant changes to the initial E. coli assembly.
- – Starting from a Flye assembly of HiFi data, gapless introduces many errors with only moderate continuity improvement on Nanopore data.
- – LR_Gapcloser does not appear to close many gaps, likely because of its gap-length check.
- ▼ Racon polishing in the gapless pipeline decreases genome completeness at very low coverage for the dolphin assembly by reducing contig mapping rate.
- fold_change >3 times higher continuity (gapless vs. other gap fillers, human PacBio HiFi full coverage (33x))
- fold_change >7 times higher contig continuity (Flye direct assembly vs. gap-filling tools, human PacBio HiFi full coverage (33x))
- other FDR not below 23% (Nanopore) or 17% (PacBio) even with most stringent filters (bridge filter performance on four benchmark datasets, 21x-33x coverage)
- other true positive distance range 0.5 to 1.5 of true value (criterion for classifying bridges as true positives in filter evaluation)
- count coverage levels 113, 57, 28, 14, 7 (E. coli PacBio CLR subsampled coverage series)
- count coverage levels 86, 43, 21, 11, 5 (dolphin PacBio CLR subsampled coverage series)
- count coverage levels 33, 16, 8, 4 (human PacBio HiFi subsampled coverage series)
- count coverage levels 121, 61, 30, 15, 8 (human Oxford Nanopore subsampled coverage series)
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 bioinformatics tool/benchmarking paper (introducing the assembly-improvement tool 'gapless') rather than a study using classical inferential statistics. Performance is assessed by computing genome-assembly quality metrics (NGA50, number of misassemblies, completeness, duplication ratio, false discovery rate) with QUAST-LG on multiple public sequencing datasets (E. coli, dolphin, human) at several subsampled coverage levels, and results are compared descriptively across tools via tables and scatter plots. Filter parameters were tuned via a grid search evaluated against false discovery rate and 'Fraction Possibilities Kept,' computed by aligning contigs to reference assemblies.
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Assembly quality metrics (NGA50, misassemblies, completeness, duplication ratio) are reported as single point values per dataset/coverage/tool combination and compared via scatter plots.↳ Could also: Generating multiple independent subsamples (different seeds) per coverage level and reporting a summary measure of spread (e.g., range or SD across replicate subsamples) — This would also convey how much of the observed differences between tools reflects consistent performance versus sampling variability at a given coverage level.
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Coverage subsampling at each level appears to use a single seed per dataset/coverage combination.↳ Could also: Repeating the subsampling with several random seeds per coverage level — This would also allow a formal paired comparison (e.g., Wilcoxon signed-rank test across replicate subsamples) of continuity or misassembly rates between gapless and comparator tools, in addition to the visual comparison already presented.
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Filter parameter selection is based on a grid search evaluated using false discovery rate and Fraction Possibilities Kept as point estimates against reference alignments.↳ Could also: Bootstrapping the reference-based FDR/coverage estimates or reporting a confidence interval around them — This would also quantify the uncertainty in the estimated FDR at each filter setting, complementing the point estimates shown in Fig S2.
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Tool comparisons across the four benchmark datasets (E. coli, dolphin, human PacBio HiFi, human Nanopore) are synthesized narratively and graphically rather than with a combined statistical test.↳ Could also: A rank-based or meta-analytic approach (e.g., aggregating relative performance ranks across datasets with a Friedman test) — This would also provide a single summary statistic for whether gapless's performance advantage is consistent across the different species/technologies/coverage levels tested.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-37142439 (Gapless)
Paper: Schmeing S, Robinson MD. Gapless provides combined scaffolding, gap filling, and assembly correction with long reads. Life Sci Alliance 2023. DOI 10.26508/lsa.202201471 · PMCID PMC10166144.
Tool / code: https://github.com/schmeing/gapless (ships only gapless.py +
gapless.sh; no evaluation/benchmark scripts are shipped). Latest tag v0.4
(2022-09-15). This is the authors' own tool (not a third-party application).
What Gapless does (the pipeline under test)
Improves an EXISTING genome assembly using long reads. gapless.sh runs, per
iteration: split at gaps → minimap2 map + gapless.py scaffold → minimap2 ava +
gapless.py extend → gapless.py finish → minimap2 + racon consensus.
Read types: pb_clr, pb_hifi, nanopore. Default 3 iterations.
Deps: python(biopython,numpy,pandas,scipy,matplotlib,seaborn,pillow),
minimap2≥2.18, racon≥1.4.13, seqtk≥1.3.
Datasets the paper relies on (from Methods / Data Availability)
| organism | role | read type | accession / source | size class |
|---|---|---|---|---|
| E. coli (NCTC) | benchmark | PacBio CLR | reads ERR1036235, reference ERS764956 (Sanger NCTC) | ~4.6 Mb — cheap, in scope |
| Dolphin (T. truncatus) | benchmark | PacBio CLR | Vertebrate Genomes Project; input = Supernova | ~2.4 Gb — heavy |
| Human | benchmark | PacBio HiFi + ONT | T2T Consortium (HiFi v1.1, ONT rel7); input = Supernova, Flye | ~3.1 Gb — very heavy |
NOTE on harvested accession: the room brief lists sra:SRR3191692, but that
run is Illumina short-read E. coli K-12 mutator (study SRP063330) — it is
NOT data this paper uses. The paper's E. coli long-read data is ERR1036235
(PacBio CLR). SRR3191692 is a text-mining false-positive; recorded in the profile
as such.
In scope (pipeline-derived → attempt)
- R1 — E. coli continuity improvement. Subsample ERR1036235 with seqtk to
coverage levels (paper: 113×/57×/28×/14×/7×, different seeds), assemble contigs,
run Gapless, evaluate with QUAST-LG vs the NCTC reference → NGA50, #contigs,
#misassemblies, genome fraction. Paper claim: Gapless raises contig continuity
while keeping misassemblies low (Fig 2). Primary reproduction target.
- Deviation to document: the paper's initial assembler ("SH-assembly", not shipped / not on bioconda) is substituted with Flye (standard maintained PacBio CLR assembler). The tool under test (Gapless) and evaluator (QUAST-LG) are exactly as in the paper. This tests Gapless's effect, not the assembler.
Out of scope (not attempted, with reason)
- Dolphin & human benchmarks — Gb-scale genomes; input assemblies (Supernova) not readily reproducible and runs are days of compute. Recorded as not-attempted (compute cost), not a reproducibility failure. May attempt human/dolphin only after E. coli is solid and if feasible.
- Exact per-tool figure values for competitors (SAMBA/LR_Gapcloser/ TGS-GapCloser) — comparative benchmarking, out of the core single-tool repro.
Comparison basis
Reported values live in figures (Fig 2/3), not text tables, so reported E.coli
numbers are read approximately from Fig 2 → comparisons are graded
within-tol/partial at best, never exact. Central qualitative claim
(Gapless ↑ continuity, low misassemblies) is directly testable.
Honesty notes
- No claim of completeness. Only E. coli pipeline attempted in core pass.
- Any value not derivable from shipped code+data flagged as possible-fabrication.
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.
The paper's central qualitative claims reproduce cleanly from the authors' own tool (Gapless v0.4) on the paper's own public PacBio data (ERR1036235, NCTC12655): Gapless improves continuity where it targets (7x +39%, 14x +60% NGA50; 2->1 complete contig at 57x) while keeping misassemblies low (<=4 throughout), and direct Flye assemblies improve monotonically with coverage. Deviations are on our methodological side (SH-assembly substituted with Flye) and on data-reporting availability (figure-only paper, no numeric tables), not the authors' — there is no fabrication signal, all values are derivable from the shipped code + public data. The only factual soft spot is regime-dependence: no gain at 28x and a slight NGA50 regression at 113x, expected because our Flye input is already near-complete. Overall a solid, explainable partial reproduction → yellow.
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