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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".