Effect of Source of DNA on the Quantitative Analysis of Genetically Engineered Traits Using Digital PCR and Real-Time PCR
Bibliographic record
Abstract
Seven commercially available DNA extraction kits were compared with a cetyltrimethylammonium bromide (CTAB) method to determine the suitability of the extracted DNA for RainDrop digital PCR (dPCR) and real-time PCR (RT-PCR) quantification of OXY235 canola, FP967 flax, and DP305423 soybean (spiked at the 0.1% level). For the kits, the highest amount of DNA extracted from a 0.2 g sample was obtained using OmniPrep for Plant for flax and DNeasy mericon Food for canola and soybean. For canola, DNA extracted with the Fast ID Genomic DNA Extraction Kit, FastDNA Spin Kit, GM Quicker 2, NucleoSpin Food, and DNeasy mericon Food was suitable for dPCR and RT-PCR. For flax, DNA extracted with Fast ID, FastDNA Spin Kit, OmniPrep for Plant, and NucleoSpin Food was suitable for RT-PCR. However, only Fast ID yielded DNA suitable for dPCR. For soybean, DNA extracted with five and six of the seven DNA extraction kits was suitable for dPCR and RT-PCR, respectively. Overall, Fast ID provided reliable results regardless of species or analysis method used. Canola, flax, and soybean DNA extracted with the CTAB method and then purified were suitable for both dPCR and RT-PCR. This is the first report showing the effect of different DNA extraction methods on the absolute quantification of genetically engineered traits using dPCR.
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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 | 0.001 |
| 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".