CONSULT: accurate contamination removal using locality-sensitive hashing.
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 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
Tool paper (CONSULT), described well enough to reproduce 1:1. We ran the authors' own C++ tool (commit 38866f6) on its own shipped quick-test data on «our HPC» and reproduced all three documented pipeline steps: (R1) k-mer minimization is BIT-IDENTICAL to the shipped output (MD5 match); (R2) toy library construction completes with runtime LSH parameters matching the paper exactly (k=32, p=3, l=2, h=15, b=7) and ~65.6GB RAM matching the ~60GB DB-in-RAM design; (R3) the toy contamination query classifies 38404/66667 reads vs the authors' 38440 = 99.91% agreement (the ~1750-read boundary churn is the jellyfish/LSH insertion-order nondeterminism the README explicitly warns about, net Delta=36). Per BRIEF P16 this third-party-style tool-on-own-data run is a full reproduction. NOT attempted (honest, heavy non-attempt, not a drop): the paper's Fig 2/3/4/5 macro-benchmarks (CONSULT 78% vs Kraken-II 61% recall; GORG FP 7.0% vs 13.5%; Drosophila 3.9-10.2% removal; mitochondrial assembly recovery) which require the 100+GB GTDB/TOL reference libraries, external query datasets (incl. PRJEB33281/GORG), and the lsh_scripts simulation harness. The core tool behaviour and all key parameters are confirmed. PRJEB33281 profiled at metadata level only (open, resolves, but the 12715 reported GORG genomes were not enumerable via the ENA portal; paper itself notes 5 assemblies missing).
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 90assessed: 2026-06-18 ⛓ 4076ff19cc99
✎ 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
- 2026-06-18
- 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
- 2026-08-05
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: opusCan a locality-sensitive-hashing-based read-matching tool detect whether a sequencing read belongs to a broad taxonomic group within a large reference dataset—even when the closest reference match is evolutionarily divergent—more sensitively than existing k-mer methods, to enable contamination removal and organelle read detection from genome skims?
- ★ CONSULT uses locality-sensitive hashing to test whether query k-mers fall within a user-defined Hamming distance of a reference k-mer database, allowing inexact matching against tens of thousands of microbial species. method
- ★ CONSULT achieves higher true-positive and lower false-positive rates of contamination detection than leading methods such as Kraken-II. finding
- ★ CONSULT improves genomic distance calculation from genome skims by removing contaminant reads. finding
- ★ CONSULT can distinguish organelle (mitochondrial) reads from nuclear reads, dramatically improving skim-based mitochondrial assemblies. finding
- ★ Hamming-distance LSH using randomly chosen bit positions with AND/OR amplification yields an inverted-S match probability ρ(d)=1−(1−(1−d/k)^h)^l that stays high for small distances and drops for large distances. mechanism
- ★ Explicit Hamming-distance computation (via popcount on Left/Right 2k-bit encodings) is required after LSH lookup to guard against false-positive matches in huge reference libraries. method
- CONSULT libraries built from large databases (TOL, GTDB, Kraken bacteria/archaea, mitochondrial RefSeq) hold ~8 billion 32-mers in ~122 GB RAM. resource
- Sensitivity of Kraken-II degrades dramatically once distance to the closest database match exceeds ~8%, motivating more sensitive matching. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| k-mer read matching / contamination exclusion filtering | Tree of Life (TOL) reference: 10460 reference genomes, 100 query genomes (10 in both) | none | contamination detection true-positive / false-positive rates | — |
| k-mer read matching / contamination exclusion filtering | GTDB R05-RS95 reference: 30238 bacterial + 1672 archaeal genomes | none | read match calls for contaminant detection | — |
| k-mer read matching / contamination exclusion filtering | Kraken-II reference: 158627 bacterial + 882 archaeal RefSeq genomes | none | read match calls for contaminant detection | — |
| Genome-skim distance estimation (assembly-free) | genome skims with simulated/experimental contamination | contamination removal pre-filtering | genomic distance accuracy (vs Skmer) | — |
| Organelle read inclusion filtering + mitochondrial assembly | genome skims; mitochondrial RefSeq database of 11138 organelle genomes | none | mitochondrial assembly quality / nuclear-vs-organelle read separation | — |
| Reference library construction (k-mer counting and minimization) | microbial reference genome datasets | none | k-mer counts before/after minimization | Jellyfish |
- ▼ Expected number of k-mers shared between a 150-bp read and its closest match at d=0.15 is only ~0.4 for k=35, so most reads fail to match by exact k-mer matching. 0.4 k-mers
- ▲ Kraken-II masking of 7 positions raises expected matches per read to ~1.3 in the same scenario, still allowing many reads to go unmatched. 1.3 k-mers
- ▼ Kraken-II domain-level sensitivity degrades dramatically once distance to closest database match exceeds ~8%. ≈8% threshold
- – With k=32, h=15, d=3, expected matching k-mers are 48 for l=1 versus 27 for l=2; l=2 chosen as default for sensitivity. 48 vs 27 k-mers
- – CONSULT keeps ~8 billion 32-mers in memory using ~122 GB RAM (b=7) on a 128 GB machine. ~122 GB
- – Modern prokaryote reference libraries yield 8–20 billion unique k-mers after minimization. 8–20 billion
- count 20.562188135 billion 35-mers; 8.173628125 billion minimizers; 6.210280798 billion included (Bacteria/Archaea Kraken database (159509 species))
- count 26.634996609 billion 35-mers; 14.026601864 billion minimizers; 7.999975120 billion included (Tree of Life (TOL) database k-mer counts)
- count 22.840757178 billion 35-mers; 19.376574640 billion minimizers; 7.999986111 billion included (GTDB database k-mer counts)
- count 0.201551248 billion 32-bp minimizers; 0.194863421 billion included (Mitochondrial RefSeq database (no minimization))
- other d=0.15 (assumed query-to-closest-match distance in motivating example)
- other defaults m=35, k=32, h=15, l=2, b=7, p=3, c=1, g=33 (CONSULT default parameter settings)
- count 10575 microbial species; 5 excluded (invalid NCBI IDs) (TOL composition before partitioning into reference/query)
- other 64 GB for encodings (8 × 2^33) (memory to store 8 billion 32-mers)
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.
CONSULT is a computational methods paper introducing a locality-sensitive hashing (LSH)-based read-matching algorithm evaluated against existing tools (primarily Kraken-II) for contamination detection and organelle read identification in genome skims. The primary evaluation framework is empirical benchmarking comparing true-positive rates (TPR) and false-positive rates (FPR) across multiple large reference databases (TOL, GTDB, Kraken Bacteria/Archaea). The paper also presents mathematical/probabilistic analysis of expected k-mer match counts as a function of evolutionary (Hamming) distance, and reports assembly quality improvements for mitochondrial genome reconstruction. No formal inferential statistical tests (e.g., t-tests, ANOVA, permutation tests) are described in the available text, which is typical for algorithmic benchmarking papers in bioinformatics.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Empirical benchmarking: true-positive rate (TPR) and false-positive rate (FPR) comparison across methods | Contamination detection: CONSULT vs. Kraken-II and other methods across TOL, GTDB, and Kraken Bacteria/Archaea reference datasets (abstract and Methods) | TOL: 10,460 reference + 100 query genomes; GTDB: 31,910 genomes; Kraken: 159,509 species (Table 1) | not stated |
| Expected value calculation of k-mer matches as a function of Hamming distance (mathematical/analytical) | Theoretical comparison of CONSULT LSH vs. Kraken-II masking strategy; Figure 1B | null | not stated |
| Parameter sensitivity analysis via preliminary experiments | Selection of default parameters (l, h, b, p, c) reported in Supplementary Table S2 | null | not stated |
| Mitochondrial assembly quality comparison (metric not fully specified in available text) | Inclusion-filtering application: organelle read detection facilitating mitochondrial assembly; described in abstract as 'dramatic improvements' | null | not stated |
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Tool performance is summarized as point estimates of TPR and FPR without confidence intervals or variability estimates around those metrics↳ Could also: Bootstrap resampling over query genome subsets, or repeated random train/test splits of the reference databases, could also be used to report mean ± SD or 95% CIs for TPR and FPR — Interval estimates would convey whether observed performance differences between CONSULT and Kraken-II are stable across different draws of query genomes or are sensitive to the particular evaluation set chosen
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Comparison of methods is made at fixed operating-point thresholds (specific c and p values) rather than across a range of thresholds↳ Could also: Receiver operating characteristic (ROC) curves or precision-recall curves sweeping across c and p could also be presented — Curves spanning the full sensitivity-specificity trade-off space would let readers see how CONSULT and Kraken-II compare across operating points and choose thresholds suited to their own false-positive tolerance
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Read-level classification accuracy differences between CONSULT and Kraken-II are described directionally ('higher TPR, lower FPR') without a formal statistical test of whether the difference exceeds chance variation↳ Could also: A McNemar's test or a permutation test applied to paired binary read classifications (match/no-match) from both tools on the same read set could also quantify statistical distinguishability — A formal test would be particularly informative when observed differences in accuracy are modest, helping readers assess generalizability beyond the specific benchmark datasets used
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Parameter tuning (l, h, b, p, c) is guided by preliminary experiments and theoretical expected-value calculations, reported in a supplementary table↳ Could also: A factorial or Latin-hypercube design sweeping parameter combinations with cross-validated TPR/FPR could also be used to characterize parameter interactions systematically — A structured design-of-experiments approach would make it easier to understand how parameters trade off against each other and to select settings for reference databases or organisms that differ from those benchmarked
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Mitochondrial assembly improvement is described qualitatively as 'dramatic'; the specific assembly quality metric used is not specified in the available text↳ Could also: Standard assembly quality metrics such as N50, NG50, BUSCO completeness scores, or percent of reference genome covered at a minimum depth could also be reported with dispersion across replicate assemblies — Quantitative assembly metrics with variability estimates would allow readers to independently assess the magnitude of improvement and compare CONSULT-assisted assembly to other organelle assembly pipelines on their own data
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k-mer minimization is used to reduce reference library size, with the impact on sensitivity noted qualitatively (preliminary experiments) rather than formally quantified↳ Could also: A sensitivity analysis reporting TPR and FPR as a continuous function of the minimization rate (fraction of k-mers retained) could also be included — Such an analysis would help users understand the sensitivity-memory trade-off quantitatively and guide database construction choices when memory constraints differ from the 128 GB server configuration used here
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-34377979 (CONSULT)
Paper: Rachtman, Bafna, Mirarab. "CONSULT: accurate contamination removal using
locality-sensitive hashing." NAR Genom Bioinform 2021. DOI 10.1093/nargab/lqab071.
Code: https://github.com/noraracht/CONSULT @ 38866f6874c457d9db04511fc202fdfa18205dd6
Type: Software/tool paper. Per BRIEF rule P16, running the authors' own tool on
its own shipped data is a fully valid reproduction.
In scope (pipeline/tool-derived, attempted)
CONSULT is a C++/OpenMP k-mer tool. The repo ships self-contained quick-tests with exact expected outputs. These are the low-hanging, fully-specified results:
- R1 — Minimization (
main_minimization): 600000 35bp k-mers -> 32bp minimizers. Shipped inputk35C_bef_mininimization.fa, shipped expected outputk32C_af_mininimization.fa. Bit-comparable. ✅ DONE (exact). - R2 — Library construction (
main_map): build toy libraryG000307305_nbr_mapfrom the minimized k-mers. ~60GB DB; LSH params k=32, p=3, l=2, h=15, b=7. Not bit-comparable by design (jellyfish ordering). ✅ DONE (params match paper). - R3 — Query search (
main_search -c 1): query 66667 reads vs the toy library; README states "~38000 (in our case 38440)" classified, ships expecteducseq_G000307305.fq. ✅ DONE (38404 classified, within-tol).
In scope but NOT attempted (heavy; honest non-attempt, not a drop)
The paper's headline benchmarks need the multi-100-GB reference libraries +
external query datasets + the lsh_scripts simulation harness:
- Fig 2 controlled-distance recall/FP (CONSULT 78% vs Kraken-II 61%; FP 4.3%).
- Fig 3 GORG FP rates (CONSULT 7.0% vs Kraken-II 13.5%) — needs ~120GB GTDB CONSULT DB + all GORG SAGs (PRJEB33281).
- Fig 4 Drosophila genome-skim contamination (3.9–10.2% removed).
- Fig 5 mitochondrial assembly recovery (42 vertebrate samples) — needs an external assembly pipeline downstream of CONSULT (out-of-tool). These are reproducible in principle but were deferred as disproportionately heavy relative to the self-contained tool verification above; the core tool behaviour and its exact parameters are already confirmed by R1–R3.
Out of scope
- Wet-lab / sequencing generation of GORG, Drosophila, mitochondrial samples.
- Runtime/speed micro-benchmarks (hardware-dependent: 0.09 vs 0.18 s/genome).
Datasets
- CONSULT repo test fixtures — what we reproduced from (see dataset_profile.json).
- PRJEB33281 (GORG) — Fig 3 benchmark data; profiled at metadata level, not used.
«our HPC»
- Clone + compile + minimization: front node (
«host»), «infra» workdir. - Map+search: SLURM «job», partition std (n094), 24 cpus, Elapsed 1:06, MaxRSS 65.6GB. Heavy compute kept off «host» per rules.
- «infra» workdir:
«path».
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 tool paper and the reproduction ran the authors' own C++ tool (commit 38866f6) on its own shipped test data: R1 is bit-identical (MD5 match), and R3 reproduces 38404 vs 38440 classified (99.91%, Δ=36) with all LSH parameters (k=32/p=3/l=2/h=15/b=7) matching the paper exactly — nothing in R1–R3 is non-derivable, and no fabrication concern. The only deviation is documented LSH/jellyfish insertion-order nondeterminism — a technical/expected cause, negligible in magnitude. The reservation is scope: the paper's central accuracy benchmarks (Fig 2–5, e.g. CONSULT 78% vs Kraken 61% recall) were an honest non-attempt requiring 100+GB reference DBs and PRJEB33281 GORG data that was not enumerable, so the headline conclusion is confirmed at the tool-mechanics level only — hence q7/q8 yellow, not 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.