Detection of Antisense RNA Transcripts by Strand-Specific RT-PCR
Bibliographic record
Abstract
Comprehensive genome annotation requires extensive cDNA analysis. This analysis has identified natural antisense transcripts (NATs), which are distinct from the microRNAs, siRNAs, and piRNAs, in a number of diverse eukaryotes. This wide conservation supports the possibility of an important role for NATs in regulating cellular processes. Investigating their roles requires the confirmation of expressed sequence tag (EST) data and the detection of antisense transcripts in distinct cellular backgrounds. This chapter describes the use of a reverse transcription polymerase chain reaction (RT-PCR) method for the detection of antisense transcripts. The protocol was designed to reduce the number of first strand synthesis reactions during screening for antisense transcripts through the utilization of antisense directed primers and oligo dT to prime first strand synthesis. These results are further confirmed using sense and antisense directed primers in first strand synthesis. Results indicate that optimization of the screens requires proper controls to confirm removal of gDNA contamination and to rule out self-priming as a source of first strand products.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".