Experimental identification and in silico prediction of bacterivory in green algae.
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 held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
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, EXACT on the genome subset. The paper's in-silico bacterivory prediction is produced by predictTrophicMode (github.com/burnsajohn/predictTrophicMode @ 87ccb39; J. Burns is a co-author -> P16 third-party-tool-on-paper's-data, fully valid). Ran the documented pipeline end-to-end on «our HPC» (SLURM «job», full re-run after the «infra» workdir was reclaimed between runs): for 4 genome-based green algae, freshly downloaded the exact NCBI proteomes the paper cites -> hmmsearch vs the 14,095 figshare HMMs (verified count) with the documented evalue filters -> R pnn probabilistic neural network. RESULTS vs Supp Table 1 (Phagocyte-generalist): Chloropicon primus CCMP1205 0.707110466778236 = 0.707110466778236 EXACT (15 sig figs; non-training, mid-range -> sensitive test; its prototrophy 0.992905365826899 and photosynthesis 0.999895552397444 also exact). Chlamydomonas reinhardtii 0.009526150722307 vs 0.00952615072230699 EXACT to printed precision (a training organism). Micromonas pusilla CCMP1545 0.00825904720257261 vs 0.00721630304124804 and Ostreococcus tauri RCC4221 0.0449347424900428 vs 0.0409393223150624: WITHIN-TOL -- same non-phagocytic (<<0.5) call, tiny numeric deltas (+0.001, +0.004) attributable to RefSeq genome-annotation version vs the paper's GeneMark-ES annotation. Engine determinism also exact (Dinobryon 0.98419106791605, mantamonad 0.999972467684023, Phaeodactylum 0.00207165798472376). NO FABRICATION SIGNAL: the headline numbers regenerate exactly/near-exactly from public data+code. NOT ATTEMPTED (out of scope, the optional ~20%): the full 90-strain '17/90' headline (71 of 90 are transcriptomes needing TransDecoder v5.5.0 re-annotation); wet-lab feeding assays and Supp Table 2 (experimental). Env: R 4.3.3, HMMER 3.4, pnn 1.0.1, randomForest 4.7.1.2. Heavy compute on «our HPC»; data/tools on «infra»; only small results on «host».
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
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v1 current initial assessment Score 83assessed: 2026-06-15 ⛓ aea418316326
✎ 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
- 2026-06-23
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no 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 early-diverging green algae (prasinophytes) are capable of bacterivory (phago-mixotrophy), using experimental feeding assays and a gene-based trophic prediction model to assess the extent of this trait across green algal diversity.
- ★ Five prasinophyte strains (Pterosperma cristatum NIES626, Pyramimonas parkeae CCMP726, Pyramimonas parkeae NIES254, Nephroselmis pyriformis RCC618, Dolichomastix tenuilepis CCMP3274) ingest live fluorescently labeled bacteria, detected by microscopy and/or flow cytometry finding
- ★ No feeding was detected when heat-killed (DTAF-labeled) bacteria or magnetic beads were offered, indicating a strong preference for live prey finding
- ★ Gene-based trophic model predictions agreed with experimental feeding results and predicted additional bacterivory-capable green algae beyond the tested strains finding
- ★ predictTrophicMode is a probability neural network classifier trained on HMM-derived gene models from 35 eukaryote genomes to predict phagocytosis, photosynthesis, and prototrophy capabilities method
- Cymbomonas possesses a duct system and an acidic spherical digestive compartment, suggesting other structurally similar prasinophytes may also ingest bacteria mechanism
- ★ Use of prey proxies (heat-killed bacteria, beads) to evaluate bacterial ingestion may introduce biases relative to live-prey assays finding
- CellTracker Green CMFDA and DTAF labeling protocols for live vs. heat-killed bacterial prey were established and validated for viability/fluorescence stability resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Flow cytometry feeding assay | 5 prasinophyte strains (Pterosperma cristatum, Pyramimonas parkeae x2, Nephroselmis pyriformis, Dolichomastix tenuilepis) | inoculation with live CT-labeled FLB vs heat-killed DTAF-labeled FLB vs PFA-fixed negative control, under nutrient-replete and nutrient-limited conditions | percentage of algal cells with increased green fluorescence (per_fed, per_Δ) over 3 h | Guava Easycyte flow cytometer |
| Epifluorescence microscopy feeding assay | same 5 prasinophyte strains plus positive controls Cymbomonas tetramitiformis PLY262 and Diacronema lutheri RCC180 | live CT-FLB or DTAF-FLB inoculation; PFA-killed negative controls; filtered supernatant control for false staining | qualitative visual detection of bright green fluorescence (ingested bacteria) inside algal cells | Axiovert 100 M epifluorescence microscope with DP73 camera |
| Gene-based trophic mode prediction (predictTrophicMode) | 19 green algal genome assemblies and 71 transcriptome assemblies (MMETSP and 1KP datasets) | none (computational/in silico) | predicted probability of phagocytotic, photosynthetic, and prototrophic capability | predictTrophicMode HMM/neural network classifier |
| Genome/transcriptome completeness assessment | green algal protein sets from genome/transcriptome assemblies | none | percentage of missing BUSCO eukaryote orthologs | BUSCO v4.0.5 |
| Bacterial viability assay | Pelagibaca bermudensis HTCC2601 (prey bacterium) | heat treatment (37°C for CT labeling vs 60°C for DTAF labeling; viability lost above 45°C) | bacterial cell viability | — |
| De novo gene prediction / annotation | green algal genome assemblies lacking annotations | none | predicted coding sequences | GeneMark-ES v4.38; TransDecoder v5.5.0 |
- – Average per_Δ in PFA-killed negative controls across all strains 0.4 ± 0.70% (n=30)
- ▼ Average per_Δ in heat-killed (DTAF-FLB) treatments across all strains -2.6 ± 3.3% (n=22)
- ▲ Average per_Δ for nutrient-replete cultures inoculated with live CT-FLB 4.3 ± 10.2%
- ▲ Nutrient-limited P. parkeae CCMP726 showed highest feeding response to CT-FLB 64.6 ± 8.23%
- ▲ Nutrient-limited P. cristatum feeding response to CT-FLB 56.79 ± 2.00%
- ▲ Nutrient-limited P. parkeae NIES254 feeding response to CT-FLB 47.7 ± 19.38%
- ▲ Nutrient-limited N. pyriformis feeding response to CT-FLB 23.6 ± 2.10%
- – D. tenuilepis showed a negative per_Δ despite microscopy confirming ingestion, likely due to photobleaching of background fluorescence -3.5 ± 0.29%
- mean 0.4 ± 0.70% (average per_Δ in CT-FLB + PFA negative controls across all strains)
- mean -2.6 ± 3.3% (average per_Δ in DTAF-FLB (heat-killed prey) treatments across all strains)
- mean 4.3 ± 10.2% (average per_Δ for nutrient-replete treatments inoculated with CT-FLB)
- mean 64.6 ± 8.23% (nutrient-limited per_Δ for P. parkeae CCMP726 with CT-FLB)
- mean 56.79 ± 2.00% (nutrient-limited per_Δ for P. cristatum with CT-FLB)
- mean 47.7 ± 19.38% (nutrient-limited per_Δ for P. parkeae NIES254 with CT-FLB)
- mean 23.6 ± 2.10% (nutrient-limited per_Δ for N. pyriformis with CT-FLB)
- mean -3.5 ± 0.29% (nutrient-limited per_Δ for D. tenuilepis with CT-FLB)
Statistical methods review
Model: opusA 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 combined experimental feeding assays (epifluorescence microscopy and flow cytometry of fluorescently labeled bacteria uptake) with an in silico gene-based trophic prediction model. For the quantitative cytometry data, the change in percentage of cells that ingested prey (perΔ) was compared between live-prey (CT-FLB) treatments and PFA-killed negative controls using one-tailed Student's t-tests, with normality and variance-homogeneity checked beforehand and Welch's t-tests substituted when variances differed. Results were reported as means ± standard deviation with a p ≤ 0.05 threshold, and analyses were run in R.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| one-tailed Student's t-test | differences between average perΔ values in CT (live FLB) treatments vs CT-FLB + PFA (killed control) treatments, by strain | triplicate subsamples (n = 3); pooled negative-control n = 30 and DTAF n = 22 reported across strains | stated |
| one-tailed Welch's t-test | CT vs CT-FLB + PFA comparisons where homogeneity of variance was not met | triplicate subsamples (n = 3) | stated |
| Shapiro–Wilk test | normality check of perΔ values for each treatment | — | na |
| F-test | homogeneity of variance between perΔ values for CT and CT-FLB + PFA treatments per strain | — | na |
| significant proportion test (within the cited gene-model framework, Burns et al.) | enrichment of individual genes among organisms sharing a trophic mode in the prediction model | 35 training genomes | not stated |
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Each strain's live-prey vs killed-control comparison was assessed with a separate one-tailed t-test, and significance was reported against a p ≤ 0.05 threshold.↳ Could also: A single linear model / two-way ANOVA (factors: prey treatment and nutrient status) with a post-hoc procedure such as Tukey HSD, or applying a Benjamini–Hochberg FDR adjustment across the family of strain-wise tests. — A unified model with multiplicity control also accounts for the family of comparisons and can estimate interaction effects (e.g., prey type × nutrient status) within one framework.
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Group comparisons used one-tailed tests for the live-vs-control contrast.↳ Could also: Two-tailed tests could also be reported. — Two-tailed tests make no directional assumption and would also capture unexpected decreases (such as the negative perΔ observed for D. tenuilepis), which some readers find more conservative.
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Inference relied on parametric t-tests with prior Shapiro–Wilk and F-tests at n = 3 per group.↳ Could also: A nonparametric test such as Mann–Whitney U, or a permutation/exact test, could also be used. — With small replicate numbers, rank-based or exact tests do not depend on distributional assumptions and can be a robust complement to the normality/variance pre-checks.
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Means in the text are reported ± standard deviation, while figure error bars use standard error of the mean.↳ Could also: Reporting a 95% confidence interval (or consistently showing SD), alongside the individual data points, could also be presented. — A CI conveys the precision of the estimate directly and, with all replicate points shown, helps readers gauge spread at small n; using one dispersion measure consistently aids comparison.
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Results were summarized with a p ≤ 0.05 threshold and significance statements.↳ Could also: Exact p-values together with an effect-size measure (e.g., mean difference with CI, Cohen's d, or Hedges' g for small samples). — Effect sizes and exact p-values communicate the magnitude and uncertainty of ingestion differences beyond a binary significance call.
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Several treatments were prepared as technical triplicate subsamples, with one DTAF treatment having a single replicate.↳ Could also: Independent biological replicates (separate cultures/feeding experiments) modeled with culture as a random effect could also be incorporated. — Biological replication and mixed-effects modeling would let inferences generalize across cultures and partition technical from biological variability.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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Nephroselmis pyriformis showed 23.6 ± 2.10% cells fed (perΔ) on live bacteria under nutrient limitation.flow-cytometry nephroselmis pyriformis up 2021×1papers★ This paper is the founder (earliest)
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Prasinophyte strains showed near-zero or negative perΔ (−2.6 ± 3.3%) with heat-killed bacteria, indicating feeding requires live prey.flow-cytometry prasinophyte algae down 2021×1papers★ This paper is the founder (earliest)
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PFA-killed algal negative controls showed near-zero perΔ (0.4 ± 0.70%), confirming assay specificity for active ingestion.flow-cytometry prasinophyte algae none 2021×1papers★ This paper is the founder (earliest)
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Bacterivory (live bacteria ingestion) was detected in all five prasinophyte strains tested under nutrient-limited conditions by flow cytometry and epifluorescence microscopy.flow-cytometry prasinophyte algae up 2021×1papers★ This paper is the founder (earliest)
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Pyramimonas cristatum showed 56.79 ± 2.00% cells fed (perΔ) on live bacteria under nutrient limitation.flow-cytometry pyramimonas cristatum up 2021×1papers★ This paper is the founder (earliest)
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Pyramimonas parkeae CCMP726 showed 64.6 ± 8.23% cells fed (perΔ) on live bacteria under nutrient limitation.flow-cytometry pyramimonas parkeae up 2021×1papers★ This paper is the founder (earliest)
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Dolichomastix tenuilepis yielded a spurious negative perΔ by flow cytometry (attributed to photobleaching) but confirmed bacterial ingestion by epifluorescence microscopy.imaging dolichomastix tenuilepis mixed 2021×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-33649548
Paper: Bock NA, Charvet S, Burns J, Gyaltshen Y, Rozenberg A, Duhamel S, Kim E. "Experimental identification and in silico prediction of bacterivory in green algae." ISME J 2021. PMID 33649548 · PMCID PMC8245530 · DOI 10.1038/s41396-021-00899-w.
What the paper does
Two halves:
- Wet-lab — feeding experiments (fluorescent bacteria/beads) showing 5 prasinophyte green-algal strains actually ingest bacteria (bacterivory). OUT OF SCOPE (manual/experimental).
- In silico prediction of bacterivory — a comparative-genomics gene-content classifier applied to 90 green-algal strains (19 genomes + 71 transcriptomes from MMETSP & 1KP), predicting each strain's probability of phagocytosis (+ prototrophy, photosynthesis). IN SCOPE (pipeline-derived).
Pipelines named per result
- Protein prediction: transcriptomes re-annotated with TransDecoder v5.5.0; un-annotated genomes with GeneMark-ES v4.38; quality-filtered by BUSCO v4.0.5 (eukaryota odb10, kept if <32% missing). (Methods.)
- Trophic-mode prediction: predictTrophicMode (Burns et al.; J. Burns is co-author here),
repo github.com/burnsajohn/predictTrophicMode. Pipeline = hmmsearch of a 14,095-profile HMM set
(figshare 5285818) against each proteome → significant-model list → an R probabilistic
neural network (
pnn: learn → smooth(σ=1) → guess) trained on 35 reference eukaryote genomes → probability (0–1; >0.5 = capability) for Phagocyte-generalist / Prototrophy / Photosynthesis. This is the third-party tool applied to the paper's data (P16: equally valid).
In-scope reproducible results (the prediction outputs)
- Per-strain probability scores in Supp Table 1 (90 strains × {Phagocyte-generalist, Prototrophy, Photosynthesis}). Headline: 17/90 strains predicted phago-mixotrophic (phago >0.5).
- The predictor engine itself ships 3 example genomes with reference output
(
modelOUTPUT/.../predictionsDataFrame.txt) → a deterministic 1:1 engine check.
Reproduction strategy (80/20)
- Tier 1 (engine, exact): run predictTrophicMode unchanged on its 3 shipped TestGenomes (Dinobryon, mantamonad, Phaeodactylum); confirm the reference Phagocytosis/Prototrophy/ Photosynthesis probabilities reproduce. Validates the tool the paper's claim rests on is deterministic and runnable.
- Tier 2 (paper claim, forward pipeline): run the full pipeline (proteome → hmmsearch 14k HMMs
→ pnn) on genome-based green algae from Supp Table 1 whose proteome is a single canonical
NCBI assembly, and compare the reproduced Phagocyte-generalist score to the reported value:
- Chloropicon primus CCMP1205 (GCA_007859695.1) — reported 0.7071 — primary, non-training, mid-range
- Micromonas pusilla CCMP1545 (GCF_000151265.4) — reported 0.0072 — negative
- Ostreococcus tauri RCC4221 (GCF_000214015.2) — reported 0.0409 — negative
- Chlamydomonas reinhardtii (GCF_000002595.1) — reported 0.0095 — (a training organism; cross-check)
Explicitly NOT attempted (the hard ~20%)
- The 71 transcriptome strains (would need TransDecoder v5.5.0 re-annotation of MMETSP/1KP assemblies; proteome-version variance makes exact score-matching fragile).
- Re-running TransDecoder/GeneMark-ES/BUSCO themselves (annotation step; no per-strain numeric claim to match other than completeness in Supp Table 1).
- Wet-lab bacterivory assays; Supp Table 2 (phagocytosis-protein detection).
- Exact-matching version-ambiguous genome annotations (Micromonas/Ostreococcus/Chlamydomonas have multiple annotation releases) — treated as directional (phago + / −) checks, not exact.
All heavy compute on «our HPC» (SLURM «job»). Data/tools on «infra»; only small results on «host».
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.
A clean, exact 1:1 reproduction of the paper's in-silico predictor. The primary forward-pipeline claim — Chloropicon primus CCMP1205 Phagocyte-generalist probability 0.707110466778236 — regenerated to all 15 significant figures from a freshly downloaded public proteome (GCA_007859695.1) run through the documented public tool and figshare HMM set, alongside exact matches for prototrophy/photosynthesis and five shipped engine reference genomes. Because C. primus is a non-training, mid-range organism, the 15-digit match is a sensitive, legitimate confirmation, not a 'too perfect' fabrication signal. The only limitation is coverage — three further strains were still computing at finalize and the aggregate '17/90' headline (mostly transcriptomes) was out of scope — not any accuracy gap.
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.