TBpore cluster: A novel phylogenetic pipeline for tuberculosis transmission studies using nanopore next-generation sequencing data
Bibliographic record
Abstract
BACKGROUND: Molecular typing of Mycobacterium tuberculosis complex isolates enhances understanding of tuberculosis (TB) transmission dynamics, supporting public health efforts in outbreak investigations. This study aims to validate TBpore, a novel bioinformatic pipeline for clustering TB transmission isolates using Oxford Nanopore Technology (ONT) data and comparing it against conventional Mycobacterial Interspersed Repetitive-Unit Variable Number (MIRU-VNTR) typing and Illumina sequencing. METHODOLOGY/PRINCIPAL FINDINGS: This retrospective case-control study included 58 clinical isolates from two TB outbreaks in Canada, previously characterized by public health investigations and MIRU-VNTR typing. DNA extraction and sequencing were performed on both Illumina and ONT platforms. Illumina data were processed using Clockwork and psdm, while Nanopore data were analyzed with TBpore. SNP distances were used to compare clustering results across methods, with clusters defined by SNP distance thresholds of ≤5 and ≤12. Both sequencing methods showed a high degree of concordance in clustering results. All isolates from the M. africanum outbreak clustered within the defined SNP thresholds, consistent with MIRU-VNTR and epidemiological data. In the M. tuberculosis outbreak, 20 out of 21 isolates clustered similarly across methods, with one exception. Within outbreak pairwise SNP distances were lower with Nanopore. CONCLUSION/SIGNIFICANCE: ONT sequencing and the TBpore pipeline offer an accurate alternative to Illumina technology for TB molecular epidemiology. This study suggests potential increased clustering sensitivity with Nanopore technology, warranting further validation on larger datasets with robust epidemiological metadata.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".