High throughput rapid amplicon sequencing for multilocus sequence typing of M. ovipneumoniae using DNA obtained from clinical samples
Bibliographic record
Abstract
Abstract Background Spillover events of Mycoplasma ovipneumoniae have devastating effects on wild bighorn sheep populations. Multilocus sequence typing (MLST), a common method for tracking bacterial lineages, is used to monitor spillover events and the spread of M. ovipneumoniae between populations. Most work involving M. ovipneumoniae typing has used Sanger sequencing, however, this technology is time consuming, expensive, and is not well suited to efficient batch sample processing. Our study aimed to develop and validate a workflow for multilocus sequence typing of M. ovipneumoniae using Nanopore Rapid Barcoding sequencing and multiplex PCR. We compare the workflow with Nanopore Native Barcoding library preparation and Illumina MiSeq amplicon protocols to determine the most accurate and cost-effective method for sequencing multiplex amplicons. Results A multiplex PCR was optimized for four housekeeping genes of M. ovipneumoniae using archived DNA samples from wild sheep. Sequences recovered from Nanopore Rapid Barcoding correctly identified all MLST types with the shortest total workflow time, and lowest cost per sample when compared to Nanopore Native Barcoding, and Illumina MiSeq methods. Conclusion Our proposed workflow serves as a convenient and effective diagnostic method for strain typing of M. ovipneumoniae, and could be applied to other bacterial MLST schemes. The workflow is suitable for diagnostic settings where reduced hands-on time, cost and multiplexing capabilities are important.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".