Development of a long-read NGS workflow for improved characterization of fastidious respiratory mycoplasmas
Bibliographic record
Abstract
Abstract Mycoplasmas are respiratory pathogens in humans and animals and due to their fastidious nature, they have been historically underdiagnosed. Lack of standardised diagnostic, typing and antimicrobial susceptibility testing methods makes clinical management and epidemiological studies challenging. The aim of this study was to develop a cost-effective and accurate sequencing workflow for genotypic characterization of clinical isolates of respiratory mycoplasmas using a rapid long-read sequencing platform. Critical aspects of bacterial whole genome sequencing were explored using fastidious respiratory Mycoplasma ( M. felis and M. cynos ) isolated from animals including: (i) four solid and liquid-based media based on a specialized formulation for Mycoplasma culture, (ii) three DNA extraction methods modified for sequencing purposes, and (iii) two de novo assembly platforms as key components of a bioinformatic pipeline including Flye and Canu assemblers. DNA quality and quantity compatible with long-read sequencing requirements were obtained with culture volumes of 160ml in modified Hayflick’s broth incubated for 96 hours. The other three culture approaches investigated did not meet the DNA quality criteria required for long-read sequencing. The use of bead-beating bacterial cell lysis in the extraction protocol resulted in smaller fragments and shorter reads compared to enzymatic lysis methods. Overall, Flye generated more contiguous assemblies than the Canu assembler. This novel study provides a step-by-step sequencing workflow including mycoplasma culture, DNA extraction and de novo assembly approaches for the characterization of highly fastidious respiratory mycoplasmas. This workflow will provide diagnosticians, epidemiologists, and researchers with a more comprehensive tool than the laborious conventional methods for a complete genomic characterization of respiratory mycoplasmas.
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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) | 0.002 | 0.002 |
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".