Genomic capacities for Reactive Oxygen Species metabolism across marine phytoplankton.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
- ✓Reported values were directly comparable
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🔴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
PARTIAL. Omar et al. 2023 (PLoS ONE) is described well enough and its public code (github FundyPhytoPhys/ROS_bioinfo, commit fc6e1e9) was run on «our HPC» in two phases. WHAT REPRODUCED: (1) N=146 organisms EXACT from shipped MergedData.csv; (2) the UPSTREAM eggNOG-mapper annotation pipeline reproduces the shipped per-organism ROS gene counts EXACTLY - a fresh eggNOG-mapper 2.1.12/eggNOG 5.0.2 annotation of the re-downloaded Phaeodactylum tricornutum proteome (NCBI GCF_000150955.2, 8825 proteins) regenerates all 8 shipped ROS-EC per-enzyme counts exactly (catalase, SOD, APX, GPx, CCP, catalase-peroxidase, glycolate-ox, sulfite-ox); (3) the qualitative direction of every headline trend (fewer ROS genes per total gene as cell size grows); (4) the NitOx-scavenging non-significance. WHAT DID NOT: running the authors' OWN GLM code on the shipped MergedData.csv does NOT regenerate the published regression statistics - every headline slope (H2O2 production/scavenging, superoxide scavenging, NO production) is 1.5-2.7x steeper and higher pseudo-R2 than published, and the NitOx-production significance call FLIPS (published n.s. -> shipped-data significant). ROOT CAUSE, now localized by Phase 2: the discrepancy is in the shipped-count-table -> published-model step, NOT the annotation pipeline (which reproduces). A broken data-versioning chain is the most likely benign explanation: the cited Zenodo DOI is wrong (resolves to ggOceanMaps), the .Rds model inputs that rendered the paper are gitignored/absent, the three shipped tables are internally inconsistent (organisms.csv=119, MergedData=146, AutomatedCounts=185 files), the catalase-bearing S. elongatus PCC11802 named in the text is absent from the shipped tables, and MetaData.csv FileName<->accession is misaligned for many rows. NOT proven fabrication - because the annotation pipeline reproduces, the published counts are plausibly real but from a different data snapshot than the one shipped. NOT attempted: full 185-organism re-annotation (subset only; raw inputs undeposited), wet-lab pilots and the H2O2 diffusion sub-study (out of scope). A human reviewer should sign off on every provisional grade.
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Assessment versions
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v1 current initial assessment Score 29assessed: 2026-06-20 ⛓ 12d7c8400246
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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
- 2026-06-22
- 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: sonnetThe authors hypothesized that cell radius (and additional traits like colony formation, flagella, and diatom cell shape) influences which components of Reactive Oxygen Species (superoxide, hydrogen peroxide, nitric oxide) production and scavenging are genomically dispensable across marine phytoplankton.
- ★ Genes encoding superoxide (O2•−) scavenging are ubiquitous across phytoplankton, but their fractional gene allocation decreases with increasing cell radius, consistent with a nearly fixed core gene set. finding
- ★ Genomic allocations to both hydrogen peroxide (H2O2) production and scavenging decrease with increasing cell radius. finding
- ★ Neither nitric oxide (NO) production nor scavenging genomic allocations change with increasing cell radius, though many taxa lack the genomic capacity for NO production or scavenging entirely. finding
- ★ The probability of possessing NO production capacity decreases with increasing cell size and is influenced by flagella and colony formation. finding
- ★ The probability of possessing NO scavenging capacity increases with increasing cell size and is influenced by flagella and colony formation. finding
- Some prokaryotic picophytoplankton (e.g., Prochlorococcus) have lost all genes encoding H2O2 scavenging, consistent with the Black Queen Hypothesis. finding
- ★ An automated Snakemake pipeline using eggNOG-Mapper/DIAMOND against the eggNOG 5.0 database, cross-referenced with BRENDA enzyme annotations, was used to identify genes encoding ROS-metabolizing enzymes across genomes/transcriptomes. method
- Loss of ROS-scavenging function is only tenable for ROS species capable of crossing the cell membrane outward to be handled by other community members. mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Comparative genomics/transcriptomics (ortholog annotation) | 146 diverse marine eukaryotic phytoplankton genomes/transcriptomes (0.4–44 μm radius) | none | fractional gene allocation and presence/absence of enzymes for O2•−, H2O2, and NO production/scavenging | eggNOG-Mapper 2.0.6 with DIAMOND against eggNOG 5.0 database, Snakemake pipeline |
| Enzyme functional annotation cross-reference | Annotated ortholog EC numbers from genome/transcriptome dataset | none | classification of enzymes as natural producers/scavengers of H2O2, O2•−, or NO in vivo | BRENDA enzyme database |
| Trait metadata compilation and statistical modeling | Same 146-taxon phytoplankton dataset (cell radius, colony formation, flagella presence, diatom shape, genome/transcript size) | none | correlation/regression of gene allocation fractions and presence-probability against cell radius, flagella, colony formation, diatom shape | R/RStudio (tidyverse, dplyr, smatr, AER, logit2prob, etc.) |
- ▼ Fractional gene allocation for O2•− scavenging decreases with increasing cell radius despite universal presence of the genes
- ▼ Fractional gene allocation for H2O2 production decreases with increasing cell radius
- ▼ Fractional gene allocation for H2O2 scavenging decreases with increasing cell radius
- – NO production and scavenging gene allocation fractions show no relationship with cell radius
- ▼ Probability of NO production capacity presence decreases with cell size
- ▲ Probability of NO scavenging capacity presence increases with cell size
- count 146 genomes/transcriptomes analyzed (Total marine phytoplankton taxa included in bioinformatic pipeline)
- other cell radius data from 100% of organisms (Metadata completeness for cell radius)
- other colony formation data from 84% of organisms (Metadata completeness for colony formation trait)
- other flagella presence/absence data from 100% of organisms (Metadata completeness for flagella trait)
- other total predicted gene models from 97% of organisms (Metadata completeness for gene model counts)
- other cell shape data from 100% of diatoms (Metadata completeness for diatom cell shape)
- other annotation parameters: seed_ortholog_evalue=0.001, seed_ortholog_score=60, query_cover=20, subject_cover=0 (eggNOG-Mapper/DIAMOND annotation thresholds used in pipeline)
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.
The study is a comparative genomics/transcriptomics analysis (not an experimental manipulation) across 146 marine phytoplankton taxa spanning a large cell-size range. Genes were annotated computationally (eggNOG-Mapper/DIAMOND against the eggNOG 5.0 and BRENDA databases) and the fractional allocation of genes to ROS production/scavenging, along with presence/absence of specific capacities, was related to cell radius and other traits (flagella, colony formation, diatom shape) using R-based statistical packages. The provided text (truncated before the detailed statistics/results narrative) documents the analysis pipeline and software but does not spell out individual test statistics, p-values, or effect sizes.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Regression of fractional gene content on cell radius (exact model/family not stated in available text; 'stats' and 'AER' R packages cited) | Relationship between fraction of genome/transcriptome allocated to ROS production/scavenging (H2O2, O2•−, •NO) and cell radius | 146 phytoplankton genomes/transcriptomes (with some traits available for subsets, e.g., colony formation for 84%) | not stated |
| Standardized major axis (SMA) type regression (inferred from use of the 'smatr' R package) | Allometric-style relationships (e.g., gene fraction vs. cell radius) mentioned in hypotheses 1-5 | not stated | not stated |
| Logistic-type regression / probability modeling (inferred from use of 'logit2prob' function and 'AER' package) | Probability of presence/absence of NO production or scavenging capacity as a function of cell radius, flagella, and colony formation | not stated | not stated |
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The analysis compares gene-content fractions and trait presence/absence across 146 phytoplankton taxa using standard regression-type approaches (via 'stats', 'AER', 'smatr').↳ Could also: Phylogenetic comparative methods, such as phylogenetic generalized least squares (PGLS) or phylogenetic logistic regression, could also be used — Because species share evolutionary history, trait values across taxa are not fully independent data points; phylogenetically informed models explicitly account for this shared ancestry structure, which can be a useful complement to standard cross-species regression.
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Gene allocation is expressed as a fraction of total gene content and related to cell radius via a regression-style approach (e.g., using 'smatr' for major-axis type fitting).↳ Could also: A generalized linear model with a binomial or beta family (modeling gene counts as successes out of total gene counts) could also be used — Proportional/compositional data such as gene-fraction outcomes are often modeled directly with count-based or beta-distributed GLMs, which can better represent bounded [0,1] data and account for varying total gene counts across genomes than a continuous-response regression.
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Presence/absence of NO production or scavenging capacity was related to cell radius and other traits, apparently through a logistic-type approach (via 'logit2prob' and 'AER').↳ Could also: A mixed-effects logistic regression with taxonomic group as a random effect could also be used — Adding a random effect for lineage or taxonomic grouping can help account for non-independence among closely related taxa sharing similar trait values, complementing a fixed-effects-only logistic model.
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The study draws on multiple hypotheses (H1-H5) tested against overlapping genomic datasets and traits.↳ Could also: A false-discovery-rate procedure such as Benjamini-Hochberg could also be applied across the family of hypothesis tests — When several related hypotheses are evaluated from overlapping data, an FDR correction is a standard way to control the expected proportion of false positives across the full set of tests.
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Gene models were annotated using a single ortholog-assignment pipeline (eggNOG-Mapper/DIAMOND against eggNOG 5.0), which the authors note is a non-supervised process subject to some annotation error.↳ Could also: Sensitivity analyses using an alternative annotation database or ensemble of annotation tools (e.g., KEGG, InterProScan, or a consensus of several annotators) could also be used — Cross-validating gene calls with an independent annotation source can help characterize how much apparent variation in gene-content fractions might stem from annotation-pipeline choice rather than biology.
What was reproduced
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