Development of a long-read next generation sequencing workflow for improved characterization of fastidious respiratory mycoplasmas
Bibliographic record
Abstract
Mycoplasma cynos and Mycoplasma felis are often associated with canine and feline infectious respiratory disease in dogs and cats, respectively. Mycoplasmas have a reduced genome and dearth of many biosynthetic pathways, making them dependent on rich medium for growth. Due to this fastidious nature, mycoplasmas have been historically underdiagnosed. The aim of this study was to develop a cost-effective and accurate sequencing workflow for genotypic characterization of clinical isolates of M. cynos and M. felis using a rapid long-read sequencing platform. We explored the following critical aspects of bacterial whole genome sequencing, including: (i) five solid and liquid-based culture approaches based on a specialized media formulation for Mycoplasma culture, (ii) three DNA extraction methods modified for long-read sequencing purposes, and (iii) two de novo assembly platforms, Flye and Canu, as key components of a bioinformatics pipeline. DNA extraction method 1, a solid-phase and column-based kit with enzymatic lysis, provided the best DNA quality and concentration followed by high coverage and sequencing contiguity. This was obtained with a culture volume of 45 ml in modified Hayflick’s broth incubated for 48 h. DNA extracted directly from colonies on agar or from small broth volumes (6 ml) did not meet the criteria required for long-read sequencing. Overall, Flye generated more contiguous assemblies than the Canu assembler and was more time efficient. This 4–5 day sample-to-sequence workflow provides the scientific and clinical communities with a more comprehensive tool than laborious conventional methods for complete genomic characterization of M. cynos and M. felis clinical isolates.
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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 | 0.003 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".