Ten simple rules for implementing deep amplicon sequencing in parasitology
Bibliographic record
Abstract
Deep amplicon sequencing is transforming parasitology by enabling high-throughput profiling of parasite communities and detection of resistance-associated genetic variants. Despite its growing adoption, many researchers face challenges in implementation, and its full potential is often hindered by challenges in experimental design, including marker selection, data analysis and reproducibility. This article presents ten simple rules for applying deep amplicon sequencing in parasitology, developed through expert consensus at a deep amplicon sequencing symposium during the 2025 World Association for the Advancement of Veterinary Parasitology conference. These rules cover essential aspects from formulating research questions and choosing appropriate markers to managing data workflows and contributing to reference databases. We highlight the importance of integrating deep amplicon sequencing with traditional parasitological methods, ensuring transparent reporting and investing in capacity building. Whether you are new to deep amplicon sequencing or seeking to improve your current practices, these guidelines offer practical advice to enhance the robustness, reproducibility, and impact of your research. By adopting these principles, parasitologists can contribute to, and advance, a more reliable and collaborative scientific landscape.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".