RNA extraction and RNA-sequencing method for transcriptomic analysis of <i>Mycobacterium tuberculosis</i>
Bibliographic record
Abstract
RNA-sequencing (RNA-seq) technologies have advanced exponentially in recent years, however, the application of RNA-seq to Mycobacterium tuberculosis remains limited. We present a wet-lab and computational protocol for RNA-seq based transcriptomics that was tested on 12 replicates each of 11 clinical isolates of M. tuberculosis (n = 132) grown in vitro with and without pyrazinamide exposure. This RNA extraction method uses low-volume cultures, mechanical lysis, TRIzol™ phase separation, and column-based purification to produce high yields of pure, intact RNA followed by rRNA depletion and cDNA library preparation. The detection of unique transcripts was optimized at a sequencing depth of 15 million reads. This method detected differential RNA expression in experimental sets with and without pyrazinamide exposure, demonstrating that the method is suitable for RNA-seq applications.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".