Series 2: Development of a Multiplex Amplicon Next Generation Sequencing Assay for Rapid Assessment of Resistance-Associated Mutations in M. tuberculosis Clinical Cases
Bibliographic record
Abstract
, rapid detection of resistance-conferring mutations enables timely initiation of effective therapy. Conversely, confirming wild-type status in resistance-associated genes supports confidence in standard regimens. We developed an amplicon-based next generation sequencing (amplicon tNGS) assay on the Illumina platform targeting eight genes linked to resistance to isoniazid, rifampin, ethambutol, pyrazinamide, and fluoroquinolones. Sequencing results were analyzed using a custom bioinformatics pipeline. Forty-seven samples were used for assay development, and 37 additional samples underwent post-implementation clinical validation. Compared to whole genome sequencing (WGS), amplicon tNGS demonstrated 97.7% sensitivity, 98.9% specificity, and 98.7% overall accuracy for variant detection in targeted regions. Resistance prediction showed 79.3% concordance with WGS; discrepancies were primarily due to mutations outside of target regions. Among post-implementation samples, 27/37 passed quality metrics for all targets, with 95.7% concordance between amplicon tNGS results and final susceptibility results. This assay is now in use in our laboratory and offers significantly faster turnaround than both WGS and phenotypic methods on cultured isolates, enabling more rapid, informed treatment decisions for tuberculosis patients.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".