Detection limits of several commercial reverse transcriptase enzymes: impact on the low- and high-abundance transcript levels assessed by quantitative RT-PCR
Bibliographic record
Abstract
BACKGROUND: In functional genomics, transcript measurement is of fundamental importance. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays are the most popular technology and depend on the initial molecular step, the reverse transcription (RT). This study provides a complex overview of the influence of elements such as RT systems, amount of background RNA, and transcript abundance on the efficiency of qRT-PCR. Using qRT-PCR, we compared the efficiency of some commonly used RT systems and measured the production of PCR-amplifiable products and the influence of PCR inhibitor contents. RESULTS: The qRT-PCR assays were conducted using the TaqMan system, although we also tested the SYBR Green I chemistry, which is not compatible with all the RT systems. When dealing with low-abundance transcripts, the SuperScript II system generated more detectable molecules than the four other systems tested: Sensiscript, Omniscript, SuperScript III and PowerScript (P < 0.05). However, the Sensiscript and PowerScript systems were more efficient for detecting high-abundance transcripts in the presence of 1 to 2 mug background RNA (P < 0.05). The most striking aspect was the influence of the dilution of the RT reaction on the subsequent PCR. Indeed, some inhibition was released when diluted RT reactions were used for the quantitative PCR measurements. Furthermore, the amount of background RNA in the RT reaction was also a major component influencing a downstream step in qRT-PCR, the PCR reaction. Whereas Sensiscript was less biased, the other systems contained an important source of PCR inhibitors, interfering as much as 70% with the qRT-PCR. CONCLUSION: This study provides a complex overview of the influence of elements such as RT systems, qRTPCR chemistry, amount of background RNA, and transcript abundance on the efficiency of qRT-PCR. Whereas the most significant influencing factor is the presence of inhibitors in the RT systems, total background RNA is also a major influencing component that affects the PCR reaction. Whenever the aim of a study is to obtain a precise gene expression measurement or to profile the global transcriptome (e.g. microarray), the RT step is critical and should be examined with care.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".