Synthetic spike-in metabarcoding for plant pathogen diagnostics results in precise quantification of copy number within the genus <i>Fusarium</i>
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".