Fluorescent-increase kinetics of different fluorescent reporters used for qPCR depend on monitoring chemistry, targeted sequence, type of DNA input and PCR efficiency
Bibliographic record
Abstract
The analysis of quantitative PCR data usually does not take into account the fact that the increase in fluorescence depends on the monitoring chemistry, the input of ds-DNA or ss-cDNA, and the directionality of the targeting of probes or primers. The monitoring chemistries currently available can be categorized into six groups: (A) DNA-binding dyes; (B) hybridization probes; (C) hydrolysis probes; (D) LUX primers; (E) hairpin primers; and (F) the QZyme system. We have determined the kinetics of the increase in fluorescence for each of these groups with respect to the input of both ds-DNA and ss-cDNA. For the latter, we also evaluated mRNA and cDNA targeting probes or primers. This analysis revealed three situations. Hydrolysis probes and LUX primers, compared to DNA-binding dyes, do not require a correction of the observed quantification cycle. Hybridization probes and hairpin primers require a correction of −1 cycle (dubbed C-lag), while the QZyme system requires the C-lag correction and an efficiency-dependent C-shift correction. A PCR efficiency value can be derived from the relative increase in fluorescence in the exponential phase of the amplification curve for all monitoring chemistries. In case of hydrolysis probes, LUX primers and hairpin primers, however, this should be performed after cycle 12, and for the QZyme system after cycle 19, to keep the overestimation of the PCR efficiency below 0.5 %. The qPCR monitoring chemistries form six groups with distinct fluorescence kinetics. The displacement of the amplification curve depends on the chemistry, DNA input and probe-targeting. The observed shift in C q values can be corrected and PCR efficiencies can be derived.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".