spotter: a single-nucleotide resolution stochastic simulation model of supercoiling-mediated transcription and translation in prokaryotes.
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
Described well enough to reproduce: YES. spotter (Hacker & Elcock, NAR 2023, PMID 37602419) is the authors' own MIT-licensed single-nucleotide-resolution C simulator of supercoiling-coupled transcription/translation. Reproduced via P16: built fresh on «our HPC» («job», 10 min, gcc 14.3.0 + gsl 2.8) at commit a7ddf4ab and ran its SHIPPED paper test decks (EXAMPLES/TEST_SYSTEMS_FOR_PAPER/RPOB) with the shipped pre-derived rate files. Being a STOCHASTIC model, reproduction = matching aggregate observables over 300 seeded trajectories per condition, not byte-identical output. RESULT = 1:1 (faithful), partial on the hard last 20%. C1/C5 EXACT: both build variants compile clean, all 6 binaries run, and the README alaS tutorial runs end-to-end producing every documented output. C4 WITHIN-TOL: lone-RNAP rpoB elongation reproduces at 6.211 +/- 1.487 nt/s vs the documented 6.168/6.2 nt/s (delta +0.7%, essentially exact). C2 PARTIAL: multi-RNAP traffic reproduces at 11.507 +/- 1.094 nt/s vs reported 10.4 +/- 1.4 nt/s — central value +10.7% but INSIDE the reported error band (10.4+1.4=11.8), and the paper's KEY scientific finding is fully reproduced: multi-RNAP cooperation in a topological domain ~doubles the elongation rate vs a lone RNAP (6.21 -> 11.51 nt/s) via mutual super/under-coiling cancellation. C3 PARTIAL/context: lone-RNAP linearized-plasmid whole-path mean 6.649 nt/s; the paper's '~11 nt/s' is a peak/max (Fig 7A), not a population mean, so reported as context. This run REPRODUCED the prior run's aggregates bit-for-bit (deterministic C_RNG seeds), confirming stability. NOT ATTEMPTED (80/20): figure-exact stochastic distributions/plots; re-derivation of rate files from raw experimental data (GSE53767, GSE56720, single-molecule data); translation-coupled gene results; 4x-enzyme variant. No possible-fabrication flags — every value examined is regenerable from the shipped artifacts. All grades PROVISIONAL pending human audit.
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 77assessed: 2026-06-15 ⛓ f99dd4a8071c
✎ 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
- 2026-06-23
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no 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: sonnetNo existing stochastic simulation model can simultaneously and mechanistically represent transcription, DNA supercoiling, and translation in prokaryotes at single-nucleotide resolution; the paper presents spotter to fill this gap and to bridge single-molecule mechanistic detail with cellular-scale sequencing/proteomics data.
- ★ spotter is the first simulation model to integrate transcription, DNA supercoiling, and translation simultaneously in a single stochastic framework for prokaryotes. resource
- ★ spotter uses the Next Reaction Method (Gibson and Bruck), derived from the Gillespie algorithm, to stochastically simulate all elementary reactions. method
- ★ spotter derives sequence-specific RNAP dwell times from experimental NET-seq data combined with nearest-neighbor thermodynamic hybridization energies. method
- ★ spotter derives translation dwell times from ribosomal profiling (ribo-seq) data together with tRNA competition. method
- ★ spotter's transcription module reproduces user-input dwell times exactly for a lone RNAP transcribing without topological barriers, but supercoiling and multi-RNAP traffic cause deviations from input dwell times. finding
- ★ spotter models RNAP pausing/backtracking via a multistate scheme (elemental pause, advanced pause states P2 and P3) with RNA cleavage-mediated exit reactions. mechanism
- spotter includes a visualization toolkit that generates VMD-based movies and residue-level trajectory snapshots of simulations. resource
- spotter is implemented in C and freely available via GitHub with accompanying documentation. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| NET-seq (nascent elongating transcript sequencing) reanalysis | E. coli strain MG1655 | none | RNAP dwell time / occupancy at each genomic position | GEO accession GSE56720 (Larson et al., Greenleaf/Landick/Weissman groups) |
| Ribosome profiling (ribo-seq) data utilization | E. coli mRNA/ribosomes | none | ribosomal dwell times used to set translation elongation rates | — |
| Stochastic simulation (Next Reaction Method / Gillespie algorithm) | E. coli alaS transcription unit (single copy) | none (physiological mRNA/protein production levels) | RNAP and ribosome positions, mRNA/protein inventories, supercoiling density over simulated trajectories | spotter (custom C software) |
| Molecular visualization / movie generation from simulation trajectories | Simulated E. coli transcription unit (DNA, RNA, RNAPs, ribosomes) | none | system-level and zoomed-in visualizations of supercoiling, pause states, RNAP rotation | VMD (Visual Molecular Dynamics) |
- – Footprint sizes assigned to DNA/RNA-binding objects based on experimental data: RNAP 35 bp, topoisomerase I 50 bp, gyrase 150 bp, ribosome 30 nt. 35/50/150 bp; 30 nt
- – Transcription bubble and RNA-DNA hybrid sizes used for translocation energy calculations. 12-bp bubble; 9-nt RNA-DNA hybrid
- – An in vivo-derived NET-seq energy function for dwell times agrees well with an energy function derived from single-molecule data (per Larson et al.).
- – Elemental pause state identified experimentally is short-lived, a necessary precursor to longer-lived P2/P3 pause states. <1 s
- other RNAP footprint = 35 bp (DNA occupancy footprint assigned to RNAP)
- other Topoisomerase I footprint = 50 bp (DNA occupancy footprint)
- other Gyrase footprint = 150 bp (DNA occupancy footprint)
- other Ribosome footprint = 30 nt (mRNA occupancy footprint)
- other Transcription bubble = 12 bp; RNA-DNA hybrid = 9 nt (Parameters for pre-translocated state energy calculations at 37°C)
- other Elemental pause duration < 1 s (Lifetime of the elemental pause state before entering longer-lived pause states)
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 paper introducing spotter, a stochastic simulation model of prokaryotic transcription, DNA supercoiling, and translation. The primary statistical framework is stochastic simulation using the Next Reaction Method (an optimized variant of the Gillespie algorithm), in which every elementary molecular reaction is assigned a rate constant and events are drawn probabilistically. Simulation outputs are designed to reproduce experimental high-throughput sequencing (NET-seq, ribosomal profiling) and single-molecule measurements; the provided text contains no conventional inferential hypothesis tests, p-values, or group comparisons.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Next Reaction Method (Gibson & Bruck, derived from the Gillespie Stochastic Simulation Algorithm) — exact stochastic simulation of all elementary reactions | All simulated transcription, supercoiling, and translation reactions throughout spotter | — | not stated |
| Nearest-neighbor (NN) thermodynamic model for RNA–DNA and DNA–DNA hybridization free energies at 37°C | Calculation of pre- vs. post-translocated state energies used to derive RNAP translocation rates at each template position | — | stated |
| Sequence-based energy function (Larson et al.) applied to gene-normalized NET-seq read counts to assign position-specific RNAP dwell times | Preprocessing of in vivo NET-seq data to derive input dwell-time landscapes for transcription simulations | — | stated |
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Stochastic simulation was implemented using the Next Reaction Method of Gibson and Bruck↳ Could also: The direct Gillespie Stochastic Simulation Algorithm (SSA) or approximate tau-leaping methods could also be used — The Next Reaction Method is asymptotically more efficient than the direct SSA for large reaction networks because it avoids redundant propensity recalculations; tau-leaping offers further speed gains for networks with fast reactions at the cost of exactness — the tradeoff between computational cost and exactness informs the choice among these approaches
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RNAP dwell times were derived by applying a sequence-based energy function to in vivo NET-seq read counts, with the intent of isolating sequence-intrinsic pause propensity↳ Could also: Dwell times could also be assigned directly from single-molecule optical-trap or FRET experiments without requiring a genomic energy-function deconvolution — Single-molecule-derived rates avoid the need to statistically remove in vivo confounders (supercoiling, RNAP traffic) that the energy function attempts to separate; however, single-molecule datasets cover far fewer template sequences, so the two approaches offer complementary breadth-versus-mechanistic-purity tradeoffs
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Simulation outputs are described as being benchmarked against experimental NGS data by reproducing mean elongation rates and occupancy profiles↳ Could also: Formal quantitative goodness-of-fit statistics — such as Pearson or Spearman correlation, a Kolmogorov–Smirnov test on occupancy distributions, or likelihood-based model comparison — could also be reported — Numerical fit metrics provide a reproducible, scale-invariant summary of how closely simulated and experimental distributions agree, and facilitate objective comparison across model variants or parameter sets
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Nearest-neighbor thermodynamic parameters for 37°C were used to calculate hybridization energies for all template positions↳ Could also: Empirically fitted or machine-learning-based sequence-to-rate models trained on large RNAP single-molecule datasets could also be used to predict position-specific translocation rates — Data-driven models can capture higher-order sequence context effects beyond the nearest-neighbor approximation and may better generalize to sequences under-represented in the thermodynamic parameter training sets
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Ribosomal profiling (ribo-seq) data are used to derive codon-specific ribosomal dwell times for the translation module↳ Could also: Codon-specific dwell times could also be assigned from tRNA abundance and competition models (e.g., the extended ternary complex model) without requiring ribo-seq data — tRNA-competition-based models generalize to organisms or conditions where ribo-seq data are unavailable; ribo-seq-derived rates integrate all in vivo sources of elongation variation, so the two approaches differ in data requirements and in how much biological context they absorb into the rate estimates
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-37602419 (spotter, Hacker & Elcock, NAR 2023)
Paper: spotter: a single-nucleotide resolution stochastic simulation model of supercoiling-mediated transcription and translation in prokaryotes. Code: https://github.com/Elcock-Lab/spotter (MIT, C, makefile). This is the authors' own simulation tool — a software/methods paper. Reproduction = build the tool from source and run its shipped paper test systems to regenerate the reported simulation-derived quantities.
Nature of the artifact
spotter is a stochastic (Gillespie-style) single-nucleotide-resolution
simulator of prokaryotic transcription/translation + DNA supercoiling. Outputs are
random per RNG seed; reported figures are aggregates over many trajectories
(typically 1000). So "1:1" here means: the mean ± spread of an aggregate
observable lands on the paper's reported value, not byte-identical output.
The repo ships EXAMPLES/TEST_SYSTEMS_FOR_PAPER/{RPOB,GAPA,DUSB_FIS,MARRAB} —
the exact, already-generated rate files + consolidated input decks used for the
paper's figures. This is what makes the figures reproducible.
IN SCOPE (pipeline-derived, attempted)
| id | result | paper loc | how |
|---|---|---|---|
| C1 | spotter builds from source (GSL + C_RNG variants) and runs | repo/Methods | make on «our HPC» |
| C2 | rpoB multi-RNAP, topo domain, k_init=0.1/s, 30-min sims → mean TX elongation rate = 10.4 ± 1.4 nt/s | Fig 8C-D | run shipped rpoB.mulitple_RNAP.topo_domain.P23_release.consolidated.inp, N seeds, aggregate per-RNAP elongation rates from tx_rate_info/summary |
| C3 | rpoB lone-RNAP, linearized plasmid → elongation rate (Fig 7A linearized ≈ 11 nt/s max) | Fig 7A | run shipped rpoB.lone_RNAP.linearized_plasmid.consolidated.inp, N seeds |
| C4 | documented input normalization: lone-RNAP rpoB mean rate 6.168 nt/s (rate-file design assumption) | RPOB NOTES / Methods | sanity check of shipped rate file + lone-RNAP topo-domain run |
| C5 | alaS startup tutorial runs end-to-end (rate generation → simulation → outputs) | README | documented smoke test of full workflow |
OUT OF SCOPE / not attempted (the hard ~20%)
- Figure-exact distributions / plots (dwell-time PDFs Fig 5B, NET-seq profiles Fig 5C, kymographs Fig 6/8, ribo-seq overlays Fig 9/10, VMD movies Fig 10): qualitative shape-matching of stochastic distributions, not pinnable to a single number; skipped per 80/20.
- Derivation of the input rate files from raw experimental data (single-molecule Dekker data → pause rates; GSE53767 ribosomal profiling → codon dwell times; GSE56720 NET-seq). The repo ships the already-derived rate files; re-deriving them from the GEO/raw data is the upstream wet-data-mining step, out of scope.
- GSE53767: used by the authors to set codon dwell times / translation
efficiencies; we consume the shipped
ribo_rates.*rather than re-deriving. - Translation-coupled gene-specific results (gapA/dusB-fis/marRAB ribo-seq) — attempt only if transcription claims land cheaply; figure-overlay comparison is qualitative.
Verdict shape
Tool-paper P16 reproduction: success = builds + the shipped paper deck regenerates the reported aggregate elongation rate within the paper's own stated spread.
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.
A faithful P16 reproduction of the authors' own stochastic spotter simulator on their shipped paper decks: build and the alaS tutorial reproduce exactly (C1/C5), the lone-RNAP rpoB rate is near-exact (C4: 6.21 vs documented 6.168/6.2 nt/s, +0.7%), and the multi-RNAP traffic rate reproduces at 11.51 nt/s vs 10.4±1.4 (+10.7% on the central value but inside the reported error band). The only sizeable numeric gap, C3, is a metric-definition mismatch (the paper's ~11 nt/s is a peak, our 6.65 nt/s a whole-path mean), not a real discrepancy. All deviations are on our/stochastic side — every value is regenerable from the shipped artifacts with no fabrication signal — and the paper's central claim (multi-RNAP cooperation ~doubles elongation via supercoiling cancellation) is fully confirmed; hence solid-yellow overall rather than byte-1:1 green.
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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.