Genomic variant-identification methods may alter Mycobacterium tuberculosis transmission inferences
Bibliographic record
Abstract
Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet, there is no current consensus on how to measure genomic variation. To test the effect of the variant-identification approach on transmission inferences for Mycobacterium tuberculosis, we conducted an experiment in which five genomic epidemiology groups applied variant-identification pipelines to the same outbreak sequence data. We compared the variants identified by each group in addition to transmission and phylogenetic inferences made with each variant set. To measure the performance of commonly used variant-identification tools, we simulated an outbreak. We compared the performance of three mapping algorithms, five variant callers and two variant filters in recovering true outbreak variants. Finally, we investigated the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant-calling approaches used by different groups do not recover consistent sets of variants, which can lead to conflicting transmission inferences. Further, performance in recovering true variation varied widely across approaches. While no single variant-identification approach outperforms others in both recovering true genome-wide and outbreak-level variation, variant-identification algorithms calibrated upon real sequence data or that incorporate local reassembly outperform others in recovering true pairwise differences between isolates. The choice of variant filters contributed to extensive differences across pipelines, and applying increasingly stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. Commonly used approaches to identify M. tuberculosis genomic variation have variable performance, particularly when predicting potential transmission links from pairwise genetic distances. Phylogenetic reconstruction may be improved by less stringent variant filtering. Approaches that improve variant identification in repetitive, hypervariable regions, such as long-read assemblies, may improve transmission inference.
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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 | 0.038 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".