Genomic Sequencing from Sputum for Tuberculosis Disease Diagnosis, Lineage Determination and Drug Susceptibility Prediction
Bibliographic record
Abstract
Abstract Background Universal access to drug susceptibility testing for newly diagnosed tuberculosis patients is recommended. Access to culture-based diagnostics remains limited and targeted molecular assays are vulnerable to emerging resistance conferring mutations. Improved sample preparation protocols for direct-from-sputum sequencing of Mycobacterium tuberculosis would accelerate access to comprehensive drug susceptibility testing and molecular typing. Methods We assessed a thermo-protection buffer-based direct-from-sample M. tuberculosis whole-genome sequencing protocol. We prospectively processed and analyzed 60 acid-fast bacilli smear-positive sputum samples from tuberculosis patients in India and Madagascar. A diversity of semi-quantitative smear positivity level samples were included. Sequencing was performed using Illumina and MinION (monoplex and multiplex) technologies. We measured the impact of bacterial inoculum and sequencing platforms on M. tuberculosis genomic mean read depth, drug susceptibility prediction performance and typing accuracy. Results M. tuberculosis was identified from 88% (Illumina), 89% (MinION-monoplex) and 83% (MinION-multiplex) of samples for which sufficient DNA could be extracted. The fraction of M. tuberculosis reads from MinION sequencing was lower than from Illumina, but monoplexing grade 3+ sputum samples on MinION produced higher read depth than Illumina ( p <0.05) and MinION multiplex ( p <0.01). No significant difference in overall sensitivity and specificity of drug susceptibility predictions was seen across these sequencing modalities or within each sequencing technology when stratified by smear grade. Lineage typing agreement percentages between direct and culture-based sequencing were 85% (MinION-monoplex), 88% (Illumina) and 100% (MinION-multiplex) Conclusions M. tuberculosis direct-from-sample whole-genome sequencing remains challenging. Improved and affordable sample treatment protocols are needed prior to clinical deployment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".