Evaluation of <i>recA</i> Sequences for Identification of <i>Mycobacterium</i> Species
Bibliographic record
Abstract
16S rRNA sequence data have been used to provide a molecular basis for an accurate system for identification of members of the genus Mycobacterium. Previous studies have shown that Mycobacterium species demonstrate high levels (>94%) of 16S rRNA sequence similarity and that this method cannot differentiate between all species, i.e., M. gastri and M. kansasii. In the present study, we have used the recA gene as an alternative sequencing target in order to complement 16S rRNA sequence-based genetic identification. The recA genes of 30 Mycobacterium species were amplified by PCR, sequenced, and compared with the published recA sequences of M. tuberculosis, M. smegmatis, and M. leprae available from GenBank. By recA sequencing the species showed a lower degree of interspecies similarity than they did by 16S rRNA gene sequence analysis, ranging from 96.2% between M. gastri and M. kansasii to 75.7% between M. aurum and M. leprae. Exceptions to this were members of the M. tuberculosis complex, which were identical. Two strains of each of 27 species were tested, and the intraspecies similarity ranged from 98.7 to 100%. In addition, we identified new Mycobacterium species that contain a protein intron in their recA genes, similar to M. tuberculosis and M. leprae. We propose that recA gene sequencing offers a complementary method to 16S rRNA gene sequencing for the accurate identification of the Mycobacterium species.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".