MoDLE: high-performance stochastic modeling of DNA loop extrusion interactions.
The main results reproduced, with only marginal, non-material deviations.
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- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
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- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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
IN PROGRESS — building MoDLE (bioconda) on «our HPC» and running the documented genome-wide hg38 example simulation. Will report .cool structure + runtime vs paper's performance claims. OpenMM comparison (rho=0.93) and full Fig5 ENCODE pipeline are out of scope (heavy). Profiling GSE90994 in same pass.
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Assessment versions
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v1 current initial assessment Score 50assessed: 2026-06-19 ⛓ 80731763fc44
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- Reproduced
- 2026-06-19
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no human curator yet
- Last updated
- 2026-07-31
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: opusCan DNA loop extrusion interactions be modeled stochastically with high performance to simulate realistic genome-wide contact patterns orders of magnitude faster than existing molecular dynamics approaches?
- ★ MoDLE is a high-performance stochastic model that simulates DNA-DNA contacts from loop extrusion genome-wide in minutes using less than 1 GB of RAM resource
- ★ MoDLE accurately simulates contact maps in concordance with OpenMM molecular dynamics simulations and with Micro-C data finding
- ★ MoDLE is orders of magnitude (4000–5000 times) faster than OpenMM at simulating loop extrusion contacts finding
- ★ MoDLE models LEF binding/release/extrusion as an iterative stochastic process and extrusion barriers (e.g. CTCF sites) as a two-state Markov process method
- ★ Altering MoDLE parameters to mimic in silico CTCF and WAPL depletion reproduces expected loss of TAD insulation and more pronounced stripe/dot patterns finding
- ★ MoDLE runs efficiently on hardware ranging from laptops to high-performance computing clusters, scaling close to theoretical optimum with CPU cores finding
- Bayesian optimization with Gaussian processes can optimize CTCF binding kinetics parameters to maximize similarity with Micro-C data method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Stochastic loop extrusion simulation (MoDLE) | Human genome (GRCh38) / H1-hESC-derived barriers | none (wildtype) and in silico CTCF/WAPL depletion, LEF density/processivity/collision alterations | Simulated DNA-DNA contact frequencies (cooler format) | MoDLE CLI |
| Molecular dynamics simulation (OpenMM, 1D+3D polymer) | Synthetic genome regions (1–500 Mbp) and 10 Mbp regions on five chromosomes, H1-hESC input | none | Simulated contact maps including random polymer contacts | OpenMM (CPU on server C; GPU on server D, NVIDIA Tesla P100) |
| CTCF ChIP-seq (input data) | H1-hESC cells | none | CTCF barrier positions and occupancy/binding probabilities | — |
| RAD21 (cohesin) ChIP-seq (input data) | H1-hESC cells | none | Cohesin/LEF binding signal | — |
| Micro-C / Hi-C (reference contact data) | H1-hESC cells | none | Experimental contact frequency matrices for comparison and pixel-pattern classification | — |
| Execution time and memory benchmarking | Synthetic genomic regions 1–500 Mbp; full human genome (3088 Mbp) | varying CPU cores (1–52), multithreading on/off | Median wall clock time, CPU time, peak memory usage | Servers A–D, Laptop A (Table 1) |
- – MoDLE output and OpenMM output correlate strongly Pearson ρ = 0.93
- – MoDLE achieves similar median pixel accuracy to OpenMM in reproducing stripe/dot patterns vs Micro-C 0.69 (MoDLE) vs 0.68 (OpenMM)
- ▲ MoDLE is dramatically faster than OpenMM across compared genome regions 4000–5000x faster
- – Genome-wide human simulation with default settings completes quickly generating >370 million contacts ~40 s (server A) / ~5 min (laptop A); >370 million contacts
- ▼ MoDLE strong-scaling: human genome simulation time drops as CPU cores increase from 1 to 52 1 h 21 min (1 core) to 1 min 48 s (52 cores)
- ▲ OpenMM execution times are very long, requiring GPUs in practice 2 h 35 min (smallest region) to >41 h (250 Mbp); up to 35 h 20 min CPU below 5 Mbp
- ▼ MoDLE simulates 500 Mbp quickly with multithreading vs single thread ~1 min (52 cores) vs ~12 min (1 core)
- – In silico CTCF depletion loses TAD insulation while WAPL-depletion mimic strengthens stripe/dot patterns
- correlation Pearson ρ = 0.93 (MoDLE vs OpenMM contact output correlation)
- other median pixel accuracy 0.69 (MoDLE), 0.68 (OpenMM) (correct classification of dot/stripe pixels vs Micro-C/Hi-C)
- other 4000–5000 times faster (MoDLE speedup over OpenMM)
- count 38,815 CTCF barriers and 61,766 LEFs (genome-wide human run barriers from H1-hESC)
- count >370 million contacts (contacts generated in genome-wide default human run)
- other ~40 s (server A); ~5 min (laptop A) (genome-wide human simulation runtime)
- other 1 h 21 min to 1 min 48 s (human genome simulation time from 1 to 52 CPU cores)
- count over 500 simulation instances per chromosome; <10 MB memory per instance (default parallel simulation instances and per-instance memory)
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/software paper (MoDLE, a stochastic simulator of DNA loop extrusion) rather than a hypothesis-testing biology study. Validation was performed by comparing MoDLE's simulated output to Micro-C data and to an existing molecular-dynamics tool (OpenMM) using descriptive and correlational metrics (e.g., Pearson correlation, median pixel classification accuracy) and by benchmarking runtime/memory with repeated runs summarized as medians. Parameter optimization was performed using Bayesian optimization with Gaussian processes rather than classical inferential statistics.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Pearson correlation | comparison of MoDLE vs OpenMM simulated contact matrices (overall and diagonal-by-diagonal) | five 10-Mbp regions across five chromosomes | not stated |
| Median pixel classification accuracy (descriptive proportion, no significance test reported) | comparison of MoDLE and OpenMM output against Micro-C dot/stripe patterns (Fig. 2E) | modeled regions in H1-hESC cells | na |
| Median elapsed wall-clock time and peak memory usage (descriptive summary statistics, no formal hypothesis test reported) | runtime/memory benchmarking of MoDLE vs OpenMM across genome region sizes (Fig. 3) | 10 repeated measurements for MoDLE, 5 for OpenMM per condition | na |
| Bayesian optimization using Gaussian processes | optimization of CTCF binding kinetics parameters (P_UU, P_BB) to maximize similarity to Micro-C data | — | not stated |
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Similarity between MoDLE and OpenMM outputs was summarized with a single Pearson correlation coefficient (ρ = 0.93).↳ Could also: Reporting a confidence interval around the correlation coefficient, or a rank-based correlation (e.g., Spearman) alongside Pearson's, could also be used — A CI would convey the precision of the estimated correlation, and a rank-based measure is often used as a complement when the linearity of the relationship between contact-matrix values is uncertain.
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Benchmarking measurements were repeated a fixed number of times (10 for MoDLE, 5 for OpenMM) and summarized with medians.↳ Could also: Reporting the spread of these repeated measurements (e.g., interquartile range, min-max, or SD) alongside the median could also be used — Showing dispersion in addition to a central-tendency estimate helps convey the variability/reproducibility of runtime and memory measurements across repeated runs.
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Pixel classification accuracy for MoDLE versus OpenMM was compared descriptively (0.69 vs. 0.68) without a formal statistical test.↳ Could also: A paired comparison test (e.g., paired t-test or Wilcoxon signed-rank test across the compared regions) or a bootstrap confidence interval on the accuracy difference could also be used — Such an approach would let readers assess whether the small observed difference in accuracy is distinguishable from measurement variability across the compared genomic regions.
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Optimization of CTCF binding parameters used Bayesian optimization with Gaussian processes to minimize an objective function.↳ Could also: A grid search or classical gradient-based optimization with reported convergence diagnostics could also be used — These alternatives can offer more transparent, reproducible convergence behavior and are sometimes preferred when the objective function is cheap enough to evaluate exhaustively.
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Comparisons between MoDLE and OpenMM were framed largely in terms of overall similarity of simulated contact patterns.↳ Could also: A quantitative distance/similarity metric with an associated null distribution (e.g., permutation test comparing observed similarity to similarity expected by chance) could also be used — This would allow a formal statistical statement about whether the observed agreement between methods exceeds what would be expected under a null model.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36451166 (MoDLE)
Paper: Rossini, Kumar, Mathelier, Rognes, Paulsen. MoDLE: high-performance stochastic modeling of DNA loop extrusion interactions. Genome Biol 2022. DOI 10.1186/s13059-022-02815-7.
Tool under test: MoDLE — authors' own C++ tool (P16: own code).
Repo: https://github.com/paulsengroup/modle (bioconda: modle, latest v1.1.0).
Full paper pipeline: https://github.com/paulsengroup/2021-modle-paper-001-data-analysis
(Nextflow + Singularity; fetch_data → ENCODE chip-seq-pipeline2 → preprocess → benchmarks/optimization/heatmap).
What MoDLE does
Stochastic simulation of DNA loop-extrusion contacts genome-wide. Inputs: a
chrom.sizes file + a BED6+ extrusion-barrier file (occupancy 0–1, strand).
Output: a .cool contact matrix (5 kbp bins by default), a .log, a
_config.toml, and a _lef_1d_occupancy.bw LEF-occupancy track. Ships example
data examples/data/hg38.chrom.sizes + hg38_extrusion_barriers.bed.xz.
IN SCOPE (pipeline-derived, attempt)
| id | result | how | difficulty |
|---|---|---|---|
| C1 | MoDLE installs/builds and runs the documented example simulation, producing a valid genome-wide .cool (5 kbp) + the 4 documented output files |
bioconda install on «our HPC»; modle simulate on shipped hg38 example data |
FLOOR |
| C2 | Output cooler structure: 5 kbp resolution, correct chrom set/bin counts vs hg38.chrom.sizes, non-trivial contact count | inspect .cool with cooler/h5 |
FLOOR |
| C3 | Runtime for genome-wide human simulation (paper: ~40 s server A many-core, ~5 min laptop A 1-core). Honest 1:1 on «our HPC» EPYC, NOT same hardware | time the run on N cores + 1 core | FLOOR (hardware-different) |
| C4 | Determinism/reproducibility of MoDLE: same seed+threads → identical/near-identical contacts; default stochastic run summary stats stable | run twice, compare pixels | STRETCH |
| C5 | Advanced "deep" example (--ncells 16 --target-contact-density 10) runs and yields denser matrix |
second simulate run | STRETCH |
| C6 | Single-chromosome memory footprint (<120 MB chr1 per paper) — observe peak RSS | «path» -v |
STRETCH |
DATASET to profile
- GSE90994 — ChIP-seq CTCF + RAD21 in JM8.N4 mouse ES cells. Paper uses 6 SRA runs SRR5085152–SRR5085157 (EBI/SRA mirror), processed via ENCODE chip-seq- pipeline2 → fold-change-over-control → extrusion-barrier annotation for the HoxD Fig 5 simulation. Profile: N reported vs observed, data type, completeness, QC.
OUT OF SCOPE (not attempted, or only noted)
- OpenMM molecular-dynamics comparison (Fig 2; Pearson ρ=0.93 MoDLE vs OpenMM; "4000–5000× faster"): requires the full OpenMM polymer MD benchmark harness — a large separate simulation campaign. Not attempted (heavy; out of FLOOR/STRETCH).
- Fig 5 HoxD end-to-end (ENCODE chip-seq-pipeline2 on GSE90994 → barriers → sim → comparison): the ENCODE pipeline is a multi-day GPU/CPU campaign. We profile GSE90994 but do not re-run the full ENCODE pipeline.
- All wet-lab / experimental Micro-C/Hi-C generation (4DN datasets) — external.
Honesty notes
- MoDLE is stochastic; exact pixel values depend on RNG seed AND thread count (per docs). Runtime claims are hardware-specific → our times are a DIFFERENT machine, reported as such, never graded "exact".
- Headline biological-accuracy claim (ρ=0.93 vs OpenMM) is NOT reproduced here.
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 software/performance paper (MoDLE loop-extrusion simulator) with fully open code (bioconda/github) and a shipped hg38 example, so there is no fabrication concern and the claims are derivable in principle. However the reproduction is incomplete: status is partial/IN PROGRESS, agreement.json is not-run-yet, and all reproduced values are blank, while the quantitative validations (OpenMM rho=0.93, Fig5 ENCODE pipeline) were declared out of scope on our side. The deviation, such as it is, sits on our methodology (scope reduction + unfinished run), not the authors'. Net: a solid, low-risk setup graded yellow across the board — nothing contradicts the central claim, but nothing is yet confirmed end-to-end.
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