The development and validation of long-read ITS-1/5.8S/ITS-2 nemabiome metabarcoding for ovine gastrointestinal nematodes using Oxford Nanopore Technologies (ONT) sequencing
Bibliographic record
Abstract
Abstract ITS-2 rRNA nemabiome metabarcoding is increasingly used to characterize gastrointestinal nematode (GIN) communities. While powerful, current approaches have some limitations in their flexibility and applicability to smaller-scale studies and diagnostic use. The short read lengths provided by the Illumina platform may lack discriminatory power for some closely related species and also pose a challenge for new marker selection and primer design. To address these challenges, we have developed ITS-1/5.8S/ITS-2 rRNA Oxford Nanopore Technologies (ONT) long-read metabarcoding for ovine gastrointestinal nematodes. Samples from two previous field studies, from UK and western Canadian sheep farms were used. ITS-1/5.8S/ITS-2 long-read metabarcoding showed strong concordance with prior ITS-2 data for the major GIN species in both datasets, with minor discrepancies for some low abundance taxa mainly due to differences in reference sequence database representation. We also used the PrimerTC tool to design a new primer pair, EC1 and EC2, to minimize the amplification of off-target fungal sequences derived from fecal DNA and maximize the nematode sequence read depth when ONT ITS-1/5.8S/ITS-2 metabarcoding was applied directly to ovine fecal stool DNA. In summary, ITS-1/5.8S/ITS-2 ONT long-read nemabiome metabarcoding showed good agreement with ITS-2 metabarcoding and the use of primer pair EC1/EC2 should make the approach more tolerant of fecal contamination of parasite material, with the potential for direct application to ovine fecal DNA. Overall, these new developments should make nemabiome metabarcoding more accessible, discriminating, and flexible for both research and diagnostic applications.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".