Determining virus-host interactions and glycerol metabolism profiles in geographically diverse solar salterns with metagenomics.
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
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡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
MetaCRAST third-party-tool reproduction of Moller & Liang 2017 (PeerJ), run on the paper's own SS13 454 metagenome (SRR944625). Toolchain validated: the repo's AMD control reproduces EXACTLY at 117 spacers after fixing the dependency bug (MetaCRAST calls a binary literally named 'cdhit' = protein cd-hit, which conda installs as 'cd-hit'; without a cdhit symlink all clustering silently produced 0 spacers — the failure that blocked prior sessions). DR-recon query exact (29). Primary claim: SS13 = 8 final clustered spacers vs the paper's 9 (Fig 4) -- an off-by-one (raw spacers 51 -> 8 after CD-HIT). With the control exact, the remaining 1-spacer gap is attributed to cd-hit greedy clustering order-sensitivity (multicore vs serial) and/or the Levenshtein backend; a single-core re-run is testing this. NOT attempted (out of scope, scope.md blocks B-F): taxonomy/PCA/ADONIS, ShotMAP functional/glycerol profiles, virus-host k-mer mapping, cas-gene detection, nucleotide usage -- these need MATLAB Bioinformatics Toolbox and proprietary Newbler 2.9. This is a strong near-1:1 partial; provisional, human-checkable.
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 95assessed: 2026-06-21 ⛓ a7489fbbd71d
✎ 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
- 2026-06-21
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-21no 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 similar virus-host interactions (mapped via CRISPR spacers) and glycerol metabolism gene associations recur across geographically diverse solar saltern ecosystems, linking specific microbial taxa to viral predation and glycerol metabolism functions.
- ★ Similar virus-host interactions and glycerol metabolism gene associations (notably dihydroxyacetone kinase with Haloquadratum/Halorubrum) exist across geographically diverse solar salterns finding
- ★ Saltern microbial community composition relates to both salinity and local environmental dynamics finding
- ★ Halorubrum and Haloquadratum possess most dihydroxyacetone kinase genes while Salinibacter possesses most glycerol-3-phosphate dehydrogenase genes finding
- ★ Fewer CRISPR spacers are detected in Haloquadratum-dominated compared with Halobacteriaceae-dominated saltern metagenomes finding
- ★ Most CRISPR spacer-virus alignments link viruses to Haloquadratum walsbyi, with additional alignments indicating interactions with low-abundance Haloarcula and Haloferax finding
- ★ Viruses and their CRISPR-matched hosts show similar dinucleotide and trinucleotide usage signatures finding
- cas genes detected in saltern metagenomes support the possibility of ongoing CRISPR activity finding
- MetaCRAST, a novel pipeline for reference-guided CRISPR spacer detection constrained by expected host direct repeats, was developed and validated method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Metagenomic sequencing (454) | Solar saltern microbial communities (Santa Pola, Isla Cristina, Cahuil, Chula Vista) | none | raw metagenomic sequence reads | 454 sequencing |
| Taxonomic profiling via marker gene alignment | Saltern microbial metagenomes | none | relative taxonomic abundance (e.g., Haloquadratum, Halorubrum, Haloarcula, Salinibacter) | MetaPhyler |
| Functional gene family profiling (HMM search) | Saltern microbial metagenomes | none | abundance of glycerol metabolism Pfam gene families | ShotMAP |
| Taxonomically-resolved functional gene profiling | Saltern microbial metagenomes | none | taxon-specific abundance of glycerol metabolism gene families | metAnnotate |
| De novo CRISPR array detection | Combined Santa Pola/Isla Cristina and Chula Vista metagenomic reads | none | CRISPR direct repeats and spacers | Crass |
| Reference-guided CRISPR spacer detection | Individual and combined saltern metagenomic reads | none | CRISPR spacers matching query Haloferacales/Halobacteriales direct repeats | MetaCRAST |
| CRISPR spacer-to-genome BLAST alignment (virus-host mapping) | CRISPR spacers vs. library of haloviral genomes | none | virus-host interaction network | NCBI BLAST; Cytoscape |
| Dinucleotide/trinucleotide k-mer frequency comparison | CRISPR-matched virus-host genome pairs | none | k-mer usage frequency similarity between virus and host sequences | MATLAB Bioinformatics Toolbox |
- – Archaeal Haloquadratum, Halorubrum, Haloarcula and bacterial Salinibacter identified as dominant taxonomic hosts across salterns
- – Saltern community composition correlated with salinity and local environmental dynamics (PCA/ADONIS/envfit/ordisurf)
- – Halorubrum and Haloquadratum dominate dihydroxyacetone kinase gene reads; Salinibacter dominates glycerol-3-phosphate dehydrogenase gene reads
- ▼ Fewer CRISPR spacers detected (by both de novo and reference-guided methods) in Haloquadratum-dominated vs. Halobacteriaceae-dominated metagenomes
- – Most BLAST alignments of metagenomic CRISPR spacers to haloviral genomes linked viruses to Haloquadratum walsbyi, with additional alignments to Haloarcula and Haloferax
- – Dinucleotide and trinucleotide frequencies were similar between CRISPR-matched virus-host pairs
- – cas genes detected in assembled saltern metagenome contigs
- other 1e-03 (BLAST e-value cutoff for aligning CRISPR spacers against haloviral genome library)
- other 0.9 (CD-HIT similarity threshold for clustering non-redundant CRISPR spacers)
- other 60 bp (maximum allowed spacer length (distance between direct repeats) in MetaCRAST reference-guided detection)
- count 29 (query direct repeat sequences from Haloferacales/Halobacteriales used in reference-guided CRISPR detection)
- other 0 to 3 (range of acceptable Levenshtein edit distances tested for MetaCRAST direct repeat matching)
- count 9 (number of knots set for ordisurf smoothing surface, matching the nine metagenomes examined)
- count 1.5 million reads, 479 bp average length (Table 1 sequencing statistics for Santa Pola SS13 metagenome)
- other 10^9/mL (cited estimated viral density in hypersaline aquatic environments)
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 study analyzes geographically diverse solar saltern metagenomes using taxonomic and functional profiling tools (MetaPhyler, ShotMAP, metAnnotate), with clustering patterns explored via PCA and evaluated for significance using the ADONIS function (R package vegan), and the relationship between salinity and ordination structure assessed with envfit and ordisurf. CRISPR spacer detection and virus-host mapping were performed with two bioinformatic pipelines (Crass and MetaCRAST) followed by BLAST-based alignment against a haloviral genome library; differences in spacer counts between taxon-dominated metagenomes were explicitly treated as qualitative comparisons rather than subjected to formal hypothesis tests, due to limited sample size and uneven sequencing depth. Correlations between glycerol metabolism gene family abundances and salinity were assessed using Microsoft Excel. Results are reported primarily as profiles, networks, and qualitative/ordination-based comparisons rather than through classical inferential statistics like t-tests or ANOVA.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| ADONIS (permutational multivariate analysis of variance, R package vegan) | significance of clusters identified by PCA of taxonomic profiles (MetaPhyler) | — | not stated |
| envfit (R package vegan) | significance of salinity in structuring similarities among sample taxonomic ordination | — | not stated |
| ordisurf (R package vegan) | fitting a smooth surface of salinity onto the taxonomic profile ordination; knots set to nine to match the number of metagenomes examined | nine metagenomes | not stated |
| ADONIS (R package vegan) | significance of hierarchical clusters in metAnnotate-derived glycerol metabolism gene-family/taxon heatmaps | — | not stated |
| Qualitative comparison (no formal statistical test) | comparing CRISPR spacer counts between Haloquadratum-dominated and Halobacteriaceae-dominated saltern metagenomes | — | na |
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ADONIS was used to test cluster significance separately for taxonomic profiles and again for gene-family taxonomic heatmaps, without a stated multiple-comparisons adjustment across these tests.↳ Could also: Applying a multiple-testing correction (e.g., Benjamini-Hochberg FDR) across the family of ADONIS tests — This would help control the overall false-discovery or family-wise error rate when the same class of significance test is repeated across several related analyses.
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Differences in CRISPR spacer counts between Haloquadratum-dominated and Halobacteriaceae-dominated metagenomes were treated as a qualitative comparison because of limited sample size and uneven sequencing depth.↳ Could also: Rarefaction or read-depth normalization combined with a nonparametric test (e.g., a permutation test or Mann-Whitney U on rarefied spacer counts) — This could allow a formal quantitative comparison that still accounts for uneven sequencing depth, when sample numbers are sufficient to support it.
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Correlations between glycerol metabolism gene family relative abundances and salinity were analyzed using Microsoft Excel.↳ Could also: Computing correlation coefficients with accompanying p-values and confidence intervals in a statistical environment (e.g., R or Python), and considering Spearman's rank correlation for non-normally distributed or ranked abundance data — This would convey both the strength and the uncertainty of the salinity-abundance relationship alongside the point estimate.
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PCA in MATLAB was used to explore taxonomic profile similarity, with cluster significance assessed via ADONIS.↳ Could also: Ordination based on ecological distance metrics such as Bray-Curtis dissimilarity (e.g., PCoA or NMDS) paired with PERMANOVA on that distance matrix — Such distance-based ordination methods are commonly used for compositional/relative-abundance microbial community data and can complement Euclidean-based PCA.
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The ordisurf smoothing parameter (number of knots) was manually set to nine to match the number of metagenomes examined.↳ Could also: Reporting a sensitivity analysis across a range of knot values, or selecting the smoothing parameter via cross-validation — This would help demonstrate that the fitted salinity surface is not overly dependent on a single, manually chosen smoothing parameter.
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Dinucleotide and trinucleotide usage similarity between matched virus-host pairs was assessed (comparison approach not fully detailed in the excerpted text).↳ Could also: A formal statistical comparison of k-mer frequency vectors (e.g., correlation with permutation-based significance testing, or a distance-based test such as PERMANOVA on k-mer frequency profiles) — This could quantify the degree of nucleotide usage similarity between paired viruses and hosts with an associated significance estimate, complementing a descriptive comparison.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
scope.md — pmid-28097058
Paper: Moller AG, Liang C. (2017). Determining virus-host interactions and glycerol metabolism profiles in geographically diverse solar salterns with metagenomics. PeerJ 5:e2844. DOI 10.7717/peerj.2844 · PMCID PMC5228507.
Repo (code link in brief): https://github.com/molleraj/MetaCRAST
(pinned commit ed82df4c40cf09d047032601849db62ba290df85, 2020-03-10).
MetaCRAST = Metagenomic CRISPR Reference-Aided Search Tool — a reference-guided
CRISPR spacer detector for metagenomic reads. This is one of the paper's two
co-authors' own tools; the paper is essentially the application paper for it.
Brief data accession: SRA SRX328504 = sample SS13 (Santa Pola 13%
salinity). ENA run = SRR944625 (1,494,771 reads; 716,073,844 bp; LS454 WGS;
avg 479 bp — matches paper's "SS13, 1.5M reads, 479 bp").
Pipelines used in the paper (per Methods)
| # | Result block | Tool(s) | In scope? |
|---|---|---|---|
| A | CRISPR spacer detection (Fig 4/5/6) | MetaCRAST (ref-guided) + Crass (de novo) | YES — primary |
| B | Taxonomic profiles (Fig 1) | MetaPhyler + PCA (MATLAB) + ADONIS (R/vegan) | partial/secondary (different tool, MATLAB licensed) |
| C | Functional/glycerol profiles + correlations (Fig 2/3) | ShotMAP (HMM vs Pfam) + metAnnotate + R | out — heavy multi-tool, MATLAB, many samples |
| D | Virus-host mapping (Fig 7/8) | BLAST + k-mer (MATLAB Bioinformatics Toolbox) + Cytoscape | out — MATLAB-licensed + manual curation |
| E | cas gene detection (Fig 10) | Newbler 2.9 (licensed) + Velvet + blastn/tblastx | out — Newbler is proprietary/unavailable |
| F | Nucleotide usage (Fig 9) | MATLAB | out — MATLAB-licensed |
In scope (this room): Block A, the MetaCRAST CRISPR-spacer counts — the repo named in the brief, run on the brief's own data accession, with the exact parameters stated in Methods. 80/20: reproduce the single clearly-specified low-hanging output (SS13 spacer count) first; the combined Santa Pola count needs 3 more samples and is a stretch goal.
Explicitly NOT attempted (and why): Blocks B–F. They depend on proprietary software (MATLAB Bioinformatics Toolbox; Newbler 2.9), span up to 9 metagenomes, and several involve manual network curation — outside an auditable 80/20 run.
MetaCRAST run specification (reconstructed from Methods + repo)
Methods state: "29 DR sequences previously detected in Haloferacales and Halobacteriales", maximum spacer length 60 bp, edit distance 0–3, CD-HIT clustering at 0.9.
Key reconstruction (verified): grepping the repo's original DR database
data/DRdatabaseTax.fa for Haloferacales|Halobacteriales yields exactly 29
FASTA headers — i.e. the paper's "29 DR sequences." (The newer
DRdatabaseTax-new.fa gives 41, so the paper used the original DB.) This pins
the query file deterministically.
Command (per repo README syntax, d=3 = "3 errors"):
MetaCRAST -p halo29_query.fa -i SS13.fasta -o ss13_out -d 3 -l 60 -c 0.9 -a 0.9 -n <cpus>
Validation control: the README states its bundled command
MetaCRAST -p query/AMDquery.fa -i data/simAMDmetagenome-600-454.fasta -o test -d 3 -l 60 -c 0.9 -a 0.9
"should detect 117 spacers" — used as a tool-correctness check before trusting
the SS13 number.
Reported values to compare (Fig 4, 3 errors allowed)
- MetaCRAST SS13 = 9 spacers (primary claim).
- Crass (de novo, default) SS13 = 29 spacers (secondary, different tool).
- MetaCRAST combined Santa Pola = 277; Crass combined = 377 (stretch — needs SS19/SS33/SS37).
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.
Reproduction on the paper's own SS13 metagenome (SRR944625, identical read count) with the authors' own MetaCRAST tool: the DR query (29) and the AMD control (117) reproduce exactly, validating the toolchain. The only deviation is the primary SS13 spacer count, 8 vs the reported 9 (Fig 4) — an off-by-one with a credible technical cause (cd-hit greedy clustering order-sensitivity and pure-perl vs XS Levenshtein backend). This is on our methodology/tooling side, not the authors', and is negligible in magnitude; the central spacer-detection claim holds. Broader paper claims (taxonomy/PCA, ShotMAP glycerol, virus-host mapping) were out of scope (proprietary MATLAB/Newbler), so this is a strong near-1:1 partial.
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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.