Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe
Why this work is in the frame
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Bibliographic record
Abstract
<ns5:p> Two billion people are infected with <ns5:italic>Mycobacterium tuberculosis</ns5:italic> , leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, <ns5:italic>Mykrobe predictor</ns5:italic> , which provided offline species identification and drug resistance predictions for <ns5:italic>M. tuberculosis</ns5:italic> from whole genome sequencing (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations. </ns5:p> <ns5:p/> <ns5:p> Here we present a new tool, <ns5:italic>Mykrobe</ns5:italic> , which provides the same functionality based on a new software implementation. Improvements include i) an updated mutation catalogue giving greater sensitivity to detect pyrazinamide resistance, ii) support for user-defined resistance catalogues, iii) improved identification of non-tuberculous mycobacterial species, and iv) an updated statistical model for Oxford Nanopore Technologies sequencing data. <ns5:italic>Mykrobe</ns5:italic> is released under MIT license at https://github.com/mykrobe-tools/mykrobe. We incorporate mutation catalogues from the CRyPTIC consortium et al. (2018) and from Walker et al. (2015), and make improvements based on performance on an initial set of 3206 and an independent set of 5845 <ns5:italic>M. tuberculosis</ns5:italic> Illumina sequences. To give estimates of error rates, we use a prospectively collected dataset of 4362 <ns5:italic>M. tuberculosis isolates</ns5:italic> . Using culture based DST as the reference, we estimate <ns5:italic>Mykrobe</ns5:italic> to be 100%, 95%, 82%, 99% sensitive and 99%, 100%, 99%, 99% specific for rifampicin, isoniazid, pyrazinamide and ethambutol resistance prediction respectively. We benchmark against four other tools on 10207 (=5845+4362) samples, and also show that <ns5:italic>Mykrobe</ns5:italic> gives concordant results with nanopore data. </ns5:p> <ns5:p/> <ns5:p> We measure the ability of <ns5:italic>Mykrobe</ns5:italic> -based DST to guide personalized therapeutic regimen design in the context of complex drug susceptibility profiles, showing 94% concordance of implied regimen with that driven by phenotypic DST, higher than all other benchmarked tools. </ns5:p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| 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 it