Synthetic spike-in metabarcoding for plant pathogen diagnostics results in precise quantification of copy number within the genus <i>Fusarium</i>
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Synthetic spike-in metabarcoding (SSIM) assays generate quantitative next-generation sequencing (NGS) data, but are marred by inconsistency and have seen limited adoption. Previous efforts to develop SSIM assays have focused on the ITS and 16S rRNA genes. This study marks the first use of SSIM as a diagnostic assay to identify and quantify plant-pathogenic species within the genus Fusarium and implements it using the single-copy TEF1 gene, which has relatively uniform G + C content and length. We identified variability between species in read quality score as a key source of bias that impacts SSIM to a lesser extent than other quantitative NGS approaches. SSIM was validated against another quantitative NGS assay that utilized qPCR (qMET) to calculate the total gene copy number. The comparison showed that SSIM was both precise (R2 &gt; 0.93 for three Fusarium species) and proportional (slope ~1) in relation to qMET. Further, we applied SSIM to 24 wheat grain samples from Italy, revealing a diverse array of Fusarium species and associated mycotoxins, with SSIM demonstrating superior predictive accuracy for most toxin concentrations compared to qPCR. Our results underscore the utility of SSIM for pathogen-agnostic diagnostics, with important implications for food safety and management of mycotoxin contamination.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it