Determination of complete chromosomal haplotypes by bulk DNA sequencing.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Any deviation was negligible
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡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
Described well enough -> 1:1 reproduction. The paper's method is mLinker (C++ Ising-spin haplotype solver integrating 10x linked-reads + Hi-C), NOT dipcall (registry code_url was a harvesting artifact; dipcall only built the NA12878 benchmark). The repo ships whole-genome final-haplotype solutions for NA12878 and RPE-1; from these I recomputed every Table-2 and Results completeness/accuracy number. Total sites (2652381), phased SNVs (2319027), final accuracies (0.997/0.996) and the scaffold completeness/accuracy values reproduce EXACTLY, including the named worst chromosomes (chr19 for NA12878 at 98.5%, chr9 for RPE-1 at 96.1%). A few completeness ratios differ by ~0.3% (within-tol) due to slightly different reference-denominator subsetting. NOT attempted: upstream extract from raw BAMs (needs ~96GB linked-reads + proprietary LongRanger), dipcall reference construction, and aneuploid cancer-genome / EGA-restricted Fig5 results (qualitative, controlled-access).
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.
-
v1 current initial assessment Score 50assessed: 2026-06-18 ⛓ 109ee20b1a85
✎ 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
- 2026-06-24
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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 tests whether combining bulk long-range (linked-read) sequencing with Hi-C sequencing can computationally reconstruct complete, accurate whole-chromosome haplotypes without single-chromosome isolation.
- ★ A hierarchical computational strategy that first builds high-confidence local haplotype blocks from long-range/linked-read linkage and then concatenates them into whole-chromosome haplotypes using Hi-C contacts method
- ★ The method resolves parental chromosome haplotypes in diploid human genomes (NA12878, RPE-1) with >99% precision and >98% completeness finding
- ★ The approach can assemble the syntenic structure of rearranged chromosomes in aneuploid cancer genomes at base-pair resolution finding
- ★ Local and long-range haplotype inference are both formulated as a minimization problem solvable by steepest descent method
- ★ Aggregating Hi-C links between megabase-scale segments amplifies sparse long-range linkage signal enough to bridge gaps and low-variant-density regions mechanism
- ★ A digital karyotype of the K-562 aneuploid cancer genome was constructed from published bulk long-range and Hi-C sequencing data resource
- Linked-reads linkage range is capped by input DNA molecule size, limiting it to local phasing finding
- Hi-C linkage density decays as a power law with genomic distance and is predominantly intramolecular (cis) finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Linked-read (10x Genomics) sequencing | RPE-1 cell line | none | variant calling and local haplotype phasing | 10x Genomics linked-reads |
| Linked-read (10x Genomics) sequencing | NA12878 lymphoblastoid cell line (v1 and v2 libraries) | none | local haplotype phasing | 10x Genomics linked-reads |
| Hi-C sequencing | RPE-1 cell line | none | long-range (whole-chromosome) haplotype phasing via chromatin contact linkage | — |
| Hi-C sequencing | NA12878 cell line | none | long-range haplotype phasing | — |
| Bulk whole-genome sequencing | RPE-1 cell line | none | variant calling | — |
| PacBio circular-consensus sequencing (CCS) long reads, low-pass (11x) | RPE-1 cell line | none | local haplotype phasing | PacBio CCS |
| Single-cell sequencing | RPE-1 monosomic single cells | none | generation of reference/ground-truth haplotypes | — |
| Bulk WGS and Hi-C / cytogenetic data (aneuploid genome analysis) | Aneuploid RPE-1 cells and K-562 cancer cell line | aneuploidy/rearrangement (endogenous) | haplotype-specific sequence coverage and Hi-C contact used to resolve chromosomal alterations and construct digital karyotype | — |
- – Haplotype inference reproduces parental chromosome haplotypes with high accuracy and completeness in diploid genomes >99% precision, >98% completeness
- – Maximum range of molecular haplotype linkage from linked-reads data ~100kb (RPE-1), ~300kb (NA12878)
- ▲ Fraction of Hi-C links consistent with cis linkage >90%
- – Residual distance-independent linkage signal in linked-reads data beyond molecular size, from random barcode tagging of unrelated fragments ~50% cis/trans
- ▼ Probability that two variant sites 100kb apart are linked by Hi-C reads <10^-3
- – Haplotype solution converged within 10 rounds of iteration for all chromosomes; Chr.2 took the longest 1,000 seconds for Chr.2
- – Chosen block-switching penalty cutoffs producing high-confidence local haplotype blocks with no apparent intra-block switching error ΔE=1000 (RPE-1), ΔE=5000 (NA12878)
- – SMA region (5q13.2) segmental duplications prevent short-read resolution of variants, causing apparent low-accuracy blocks that reflect false variants rather than true switching errors ~200kb duplications, >98% sequence similarity
- other >99% precision, >98% completeness (haplotype benchmarking against reference data for NA12878 and RPE-1)
- other >90% (fraction of Hi-C links consistent with cis linkage)
- other ~50% (residual cis/trans linkage accuracy in linked-reads data beyond molecular size, reflecting random barcode collisions)
- other <10^-3 (probability of Hi-C linkage between variants 100kb apart)
- count 941,518,426 reads, 60x mean depth (RPE-1 linked-reads sequencing data)
- count 486,848,169 reads, 91,428,507 contacts (>1Mb) (NA12878 Hi-C sequencing data)
- other ΔE=1000 (RPE-1), ΔE=5000 (NA12878) (chosen block-switching penalty cutoffs for high-confidence haplotype blocks)
- other 1,000 seconds (convergence time for Chr.2 local haplotype inference, longest among all chromosomes)
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 a two-stage haplotype phasing algorithm that combines linked-read and Hi-C sequencing data. Validation was performed by benchmarking the inferred haplotypes of two diploid human genomes (NA12878 and RPE-1) against independent reference datasets using accuracy metrics (fraction of genotypes consistent with the majority haplotype per block, N50 block length, and overall precision/completeness). The paper relies exclusively on descriptive performance metrics rather than formal inferential hypothesis tests, consistent with the algorithmic benchmarking context.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Intra-block phasing accuracy (max(f, 1-f) where f = fraction of phased genotypes agreeing with reference) | Benchmarking of local haplotype blocks vs. GIAB and diploid-assembly reference haplotypes (Fig. 4, Chr.5 and genome-wide) | — | not stated |
| Fraction of Hi-C links consistent with cis linkage (descriptive proportion) | Assessment of Hi-C and linked-read molecular haplotype linkage quality across genomic distances (Fig. 2e, f) | — | not stated |
| N50 haplotype block length (descriptive summary statistic) | Comparison of haplotype block continuity across switching-penalty cutoffs and samples (Fig. 4c) | — | na |
| Overall precision and completeness (>99% and >98%) against reference haplotypes | Top-level benchmark of whole-chromosome haplotype inference for NA12878 and RPE-1 | — | not stated |
| Average number of Hi-C links between genomic segments at varying distances (descriptive mean) | Quantifying Hi-C linkage signal between 0.5, 1, and 2 Mb segments (Fig. 3a) | — | not stated |
-
Phasing accuracy is reported as a single point estimate (e.g., >99% precision) without any measure of uncertainty↳ Could also: Bootstrap resampling over chromosomal segments or binomial confidence intervals around the accuracy proportion could also be reported — Uncertainty estimates around the benchmark accuracy would allow readers to assess how stable the performance metric is and to formally compare it against competing methods at a specified confidence level
-
Validation was performed on two genomes (NA12878 and RPE-1) selected by data availability rather than a systematic sample↳ Could also: Leave-one-chromosome-out or k-fold cross-validation across chromosomes within each genome could also be used to estimate generalization error — Cross-validation would provide an internal estimate of variance in accuracy across genomic regions and reduce dependence on the specific choice of benchmark genome
-
The N50 block length and accuracy are compared across switching-penalty cutoffs visually (Fig. 4c) without a formal quantitative comparison↳ Could also: A receiver operating characteristic (ROC) or precision-recall curve plotting accuracy against completeness across the full range of cutoff values could also be constructed — A ROC/PR curve would make the accuracy-completeness tradeoff across all cutoffs explicit and would enable selection of an optimal operating point using a principled criterion such as the F1 score
-
The decay of Hi-C linkage density with genomic distance is described as following a power-law (Fig. 2a, b) based on visual inspection↳ Could also: A formal log-log linear regression with goodness-of-fit statistics (R², residual plot) could also be used to quantify how well the power-law model fits the observed decay — Quantifying the fit would allow the degree of power-law behavior to be compared across datasets or against competing decay models (e.g., exponential), and would surface any deviations at specific distance ranges
-
Performance of the method is characterized by a single threshold-based binary classification (≥98% accuracy = high-confidence block, colored gray; <98% = low-confidence, colored red)↳ Could also: A continuous scoring approach using the block-switching penalty score as a calibrated confidence measure, reported alongside the accuracy estimate, could also be used — A calibrated confidence score would give downstream users a probabilistic handle on block reliability rather than requiring a fixed accuracy threshold, enabling more flexible downstream use
-
Benchmarking uses two reference datasets for NA12878 (GIAB and diploid assembly) and one for RPE-1 (single-cell monosomy), but concordance between reference datasets is not formally quantified↳ Could also: Cohen's kappa or percent raw agreement between the two NA12878 reference haplotypes could also be reported before using them as ground truth — Quantifying inter-reference agreement would bound the ceiling on evaluable accuracy: if the two reference datasets disagree at 0.5% of sites, a measured accuracy of 99.5% cannot be distinguished from the reference noise floor
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.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
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.