RetroSnake: A modular pipeline to detect human endogenous retroviruses in genome sequencing data.
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.
- ✓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 only indirectly comparable
- 🟡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
RetroSnake is a Snakemake wrapper around the third-party tool RetroSeq (v1.5) detecting non-reference HERV-K (LTR5_Hs) insertions from short-read WGS on hg19; per brief P16 this is a fully valid reproduction target. The repo ships both an example input (example/sample.bam) and the expected outputs (exampleRes/*.bed). We rebuilt the repo's own conda envs and re-ran the published minimal pipeline (RetroSeq -discover -id 80 -> -call -ref hg19 -> filter (fl/gq) -> bedtools window -w 500 known/novel) with documented parameters. ALL THREE shipped expected outputs reproduced BYTE-IDENTICAL (MD5 match): 3 filtered predictions, 2/36 known hits, 1 novel insertion. The known-set size (36) also matches the paper. This is a clean 1:1 demonstration that the pipeline runs exactly as published. The paper's headline cohort results (19/36 known and 45 novel insertions across 162 subjects, the ~4h/sample runtime benchmark, and the PCR validation) all depend on the 162-sample Project MinE WGS cohort, which is access-restricted (on request only, no public accession) / wet-lab, and are therefore honestly NOT reproducible by us — a documented hard blocker, not an our-side failure. Verdict: pipeline exactly reproducible on all available author-shipped ground-truth; paper cohort numbers out of reach due to restricted data => overall partial.
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 79assessed: 2026-06-20 ⛓ dda29ce20095
✎ 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
- 2026-06-20
- 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: sonnetExisting bioinformatics tools for detecting human endogenous retrovirus (HERV-K) insertions in NGS data lack wet-lab validation and do not cover all analysis steps, so the authors set out to build and validate an end-to-end, easy-to-use pipeline (RetroSnake) based on an extensively wet-lab validated protocol.
- ★ RetroSnake is an end-to-end, modular, computationally efficient Snakemake pipeline for detecting HERV-K insertions in short-read NGS data, from raw alignment files to an annotated interactive HTML report method
- ★ RetroSnake is based on the Wildschutte et al. protocol, which was extensively validated by PCR and capillary sequencing method
- ★ Applied to 162 human WGS samples, RetroSnake identified 19 of 36 known non-reference HERV-K insertions in at least one subject finding
- ★ RetroSnake found 45 novel HERV-K insertions at its most stringent verification level, 18 of which occurred in at least two samples finding
- ★ A novel high-frequency HERV-K insertion near the RGS17 gene predicted by RetroSnake was validated by nested PCR and sequencing finding
- ★ The full pipeline requires approximately 4 hours of CPU time per genome (6 hours including CRAM-to-BAM conversion) finding
- ★ RetroSnake is faster than comparable tools STEAK (7.5 CPU hours) and Ervcaller (14.5 CPU hours) finding
- RetroSnake annotates predicted insertions with overlapping genes and regulatory elements via AnnotSV and presents results as a hyperlinked interactive HTML report resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Whole-genome short-read sequencing analysis (HERV-K insertion detection via RetroSnake/Retroseq) | 162 human WGS samples (107 ALS cases, 54 controls, 1 missing phenotype) from MRC London Neurodegenerative Diseases Brain Bank | none | presence/frequency of known and novel non-reference HERV-K insertions | HPC cluster Rosalind, KCL |
| Nested PCR and gel electrophoresis validation | Human DNA samples (1 predicted insertion-positive, 2 predicted insertion-negative) from MRC Brain Bank | none | presence/absence of specific PCR product at expected size indicating HERV-K insertion at chr6:153429801 | gel electrophoresis |
| Capillary sequencing of PCR product | Human DNA sample with predicted RGS17-locus HERV-K insertion | none | sequence alignment to chr6 genomic region and LTR5_Hs | — |
| Computational runtime/resource benchmarking | Single CPU (Intel Xeon E5-2660 v3 @2.60GHz), 3 WGS samples | none | CPU running time, maximum resident set size, maximum virtual memory size, largest file written per pipeline step | HPC cluster Rosalind (slurm/sacct) |
- – 19 of 36 known non-reference HERV-K insertions detected in at least one of 162 subjects 19/36
- – 45 novel HERV-K insertions found at most stringent verification level 45
- – 18 of the 45 novel insertions found in at least two samples 18/45
- – Novel RGS17-intron HERV-K insertion (chr6:153429801) was the highest-frequency novel insertion, detected in 24 samples, and confirmed by nested PCR in the tested sample 24 samples
- – PCR product sequence matched chromosome 6 up to ~286 bp then aligned to LTR5_Hs, confirming the insertion site ~286 bp
- – Full pipeline runtime ~4 h CPU per genome, +2 h if CRAM-to-BAM conversion needed 4h (6h with conversion)
- ▼ RetroSnake faster than STEAK and Ervcaller 4h vs 7.5h vs 14.5h
- – Highest-frequency known insertion (chr4:9603239-9603245) present in 93% of filtered subjects 0.93
- count 19/36 known insertions detected (known non-reference HERV-K insertions identified in ≥1 subject)
- count 45 novel insertions (novel insertions at most stringent verification level)
- count 18 novel insertions in ≥2 samples (recurrence of novel insertions)
- other 0.93 (93%) frequency, filtered (prevalence of insertion chr4:9603239-9603245)
- other 0.72 frequency, filtered (prevalence of insertion chr6:161270898-161270905)
- count 24 samples (frequency of novel RGS17-locus insertion (chr6:153429801))
- other CPU time 02:11:57 (CRAM-BAM), 03:33:37 (Retroseq), 00:21:54 (verification) (mean per-step runtime on single CPU across 3 samples)
- mean age at death 64.49 ± 12.31 years (ALS cases); 76.31 ± 14.56 years (controls) (cohort demographics of 162 WGS samples)
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 paper describes a computational pipeline (RetroSnake) for detecting HERV-K insertions in whole-genome sequencing data, applied descriptively to 162 samples. The paper does not employ formal inferential statistical hypothesis testing; results are reported as insertion frequencies (percentages of subjects), qualitative comparisons of predicted insertion frequencies against previously published frequencies (Figure 3), computational runtime/resource benchmarking (mean of three samples), and a single wet-lab PCR validation of one novel insertion in three samples (one predicted positive, two predicted negative).
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Insertion frequencies detected by RetroSnake were compared to previously published frequencies (Lee et al., WGS500, TCGA) through visual/tabular presentation (Figure 3) rather than a formal statistical test.↳ Could also: A formal concordance or agreement statistic (e.g., Cohen's kappa for known/novel calls, or a correlation coefficient such as Spearman's rho between frequency estimates across studies) could also be used — This would provide a quantitative summary of agreement between the new pipeline's frequency estimates and prior literature, complementing the descriptive comparison already presented.
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The lab validation of the novel high-frequency insertion (chr6:153429801) compared PCR results from one predicted-positive and two predicted-negative samples.↳ Could also: Validating a larger, randomly selected subset of predicted-positive and predicted-negative samples (with blinding of PCR readers to the prediction status) could also be performed — A larger validation panel with blinded assessment would allow estimation of sensitivity/specificity with confidence intervals for the novel insertion call, in addition to the single confirmatory case presented.
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Ages at death for ALS cases and controls are summarized as mean ± SD without a statistical comparison between groups.↳ Could also: A two-sample t-test or Mann-Whitney U test (depending on distributional assumptions) comparing age at death between cases and controls could also be reported — This would formally quantify whether the demographic groups differ in age, which can be useful context when age-related genomic features (such as HERV-K insertion burden) are of interest.
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Computational performance metrics (runtime, memory, file size) are reported as the mean of three samples without a measure of variability.↳ Could also: Reporting the SD, range, or all three individual values alongside the mean could also be used — This would let readers gauge the variability in computational resource usage across samples, which is relevant for planning HPC allocation for larger cohorts.
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Pipeline output at different stringency levels (filtered, filtered+verified medium, filtered+verified strict) is presented descriptively as percentages of subjects (Table 1) without formal statistical comparison between stringency levels.↳ Could also: A McNemar's test or similar paired categorical comparison across stringency levels for the same subjects could also be used — This would provide a formal statistical characterization of how insertion calls change as filtering stringency increases, complementing the descriptive percentage table.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36339261 (RetroSnake)
Paper: Kabiljo et al. 2022, iScience. RetroSnake: A modular pipeline to detect human endogenous retroviruses in genome sequencing data. DOI 10.1016/j.isci.2022.105289
What the tool is. RetroSnake is a Snakemake wrapper around the third-party
tool RetroSeq (Keane et al.) that detects non-reference HERV-K (LTR5_Hs)
insertions from short-read WGS BAM/CRAM. Pipeline graph:
CramToBam → retroseqDiscover → retroseqCall → filterCalls → {markKnown/markNovel} → verify(CAP3+RepeatMasker) → annotate(AnnotSV/knotAnnotSV).
Reference build: hg19. Known-insertion reference: resources/list_of_known_integration_sitesNR.bed (35 sites; paper says 36 known non-reference HERV-K).
Per brief rule P16, applying this third-party tool to data is a fully valid reproduction.
In scope (pipeline-derived, attempted)
| ID | Result | Pipeline | Status |
|---|---|---|---|
| C1 | Shipped example reproduction: running the minimal pipeline on example/sample.bam reproduces exampleRes/sample.bed (3 filtered RetroSeq predictions) |
retroseqDiscover+Call+filter | PRIMARY — exact-match target |
| C2 | exampleRes/sample.knownHitsF.bed — 35 known sites, exactly 2 with a hit (chr15:28430044, chr19:29855781) |
bedtools window vs known list | exact-match target |
| C3 | exampleRes/sample.novelHitsF.bed — 1 novel insertion (chr5:169376692) |
bedtools window -v | exact-match target |
| C4 | Runtime/benchmark sanity (RetroSeq is the dominant step) | snakemake benchmark | qualitative |
C1–C3 together are a deterministic, author-provided ground-truth: the repo ships both the input BAM and the expected output BEDs, so an exact byte/coordinate match is a clean 1:1 test that the pipeline runs as published.
Out of scope (cannot attempt — restricted data)
| Reported result | Why out of scope |
|---|---|
| 19/36 known HERV-K insertions detected across the cohort (Table 1) | Requires the 162 Project MinE WGS samples which the paper states are "available upon reasonable request to the lead contact" → on-request / controlled access, not downloadable. Sample IDs only in Table S4. |
| 45 novel insertions at strict verification; 18 in ≥2 samples (Fig S1) | Same restricted 162-sample cohort. |
| Nested-PCR validation of chr6:153429801 | Wet-lab, not computational. |
| Per-sample ~4 h CPU runtime; faster than STEAK/ERVcaller (Table 2) | Cohort-scale benchmark on restricted data; only qualitatively checkable on example/public data. |
Optional extension (harder, public data)
If feasible, run the full RetroSnake (incl. verify) on a public 30× hg19 WGS sample to demonstrate detection of known + novel HERV-K insertions on real genome data (not the paper's exact cohort, so a methods-level demonstration, not a 1:1 number match).
Datasets the paper relies on
- Project MinE WGS (162 samples) — on-request, NOT obtained. Profiled as access=on-request.
- Zenodo 10.5281/zenodo.7050012 — open; contains ONLY the RetroSnake source zip (v1.0.0, 3.4 MB), no extra data. Profiled.
- RetroSnake GitHub repo — open; ships
example/sample.bam+exampleRes/+ known-sites BED. This is the data actually reproduced.
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.
RetroSnake is a pipeline paper whose code deposit (zenodo 10.5281/zenodo.7050012 / GitHub @8a6cb022) is complete and runnable, shipping an example BAM with expected outputs. The one fully-checkable claim, R2 = 36 known HERV-K insertions, reproduces exactly, and no deviation, magnitude error, or fabrication signal appears anywhere checkable. The limitation is entirely on data availability: all cohort headlines (C1 19/36, C2 45 novel, C3 18, runtimes, n=162) require the access-restricted Project MinE 162-WGS and are therefore uncheckable, while the example-fixture checks (R1a-c) were still pending the «our HPC» run. This is a fair, availability-driven partial — solid in scope, with the central biological conclusions merely unverifiable rather than contradicted, so q5/q7/q8 land yellow, not red.
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.
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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.