Optimizing culture-free approaches to recover high quality <i>M. tuberculosis</i> genomic variation
Bibliographic record
Abstract
Abstract Background: Mycobacterium tuberculosis ( Mtb ) genomic epidemiology often relies on culturing patient sputum, a time and labor-intensive process. Hybrid capture approaches have been successfully used to enrich Mtb DNA from complex clinical samples, yet the accuracy of variant identification from captured samples has not been systematically evaluated. Methods: We created artificial strain mixtures of two well-characterized Mtb isolates such that the minor strain comprised 0-50% of Mtb DNA and serially diluted the Mtb DNA into human DNA to simulate diagnostic samples with different sputum smear burdens (32 samples). We also prospectively collected paired Mtb diagnostic cultures and sputum submitted to a national diagnostic laboratory (7 sample pairs). We performed hybrid capture and Illumina whole genome sequencing for all samples. For the artificial strain mixtures, we measured hybrid capture efficiency, the percentage of total reads mapping to Mtb , and performance of fixed and minority variant identification. For the diagnostic samples, we compared the number, identity of, and minor allele frequencies of minority variants identified in the cultured and hybrid captured samples. Results: In the artificial strain mixture experiment, hybrid capture efficiency was 97% when Mtb comprised 0.01% of the input DNA. Single nucleotide polymorphism (SNP) identification via hybrid capture had a sensitivity ≥ 91% and precision ≥ 97% for Mtb lineages 4.1.2.1 and 4.9, excluding PE/PPE genes, when Mtb comprised 0.01% of input DNA. Observed minor allele frequencies were closely correlated (r=0.60 to r=0.79, p < 0.001) with input minor allele frequences across all dilutions. Among paired diagnostic samples, hybrid capture efficiency was high, 95%. However, four of the seven captured sputa samples were overwhelmed with Pseudomonas contamination, which comprised >25% of sequence reads. We did not detect a significant difference in the number of minority variants identified in cultured (median: 14, range: 9-23) and hybrid captured samples (median: 22 variants, range 4-328, p=0.2) and minor allele frequences were correlated (r = 0.95, p < 0.001) Conclusions: Hybrid capture of diagnostic sputa samples efficiently generates accurate Mtb whole genome sequences and minority variant calls. Hybrid capture may offer an alternative to culture-based sequencing that could extend the coverage of genomic epidemiology studies. Impact Statement: Mycobacterium tuberculosis ( Mtb ) genomic epidemiology often relies on culturing patient sputa, which is time and labor-intensive. Hybrid capture approaches enrich Mtb DNA from complex clinical samples, yet the efficiency and accuracy of identification of both consensus and minority variants from captured samples has not been systematically evaluated. To address these questions, we assessed the accuracy of hybrid capture both in experimental strain mixtures of well-characterized Mtb isolates and in clinical diagnostic samples. We found that hybrid capture of sputa samples can be used to efficiently sequence Mtb DNA from complex mixtures and that variant identification is highly accurate. Our results suggest that hybrid capture may offer an alternative to culture-based sequencing that could extend the coverage of genomic epidemiology studies.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".