Effects of DNA Extraction and Purification Methods on Real-Time Quantitative PCR Analysis of Roundup Ready Soybean
Bibliographic record
Abstract
The quality of DNA affects the accuracy and repeatability of quantitative PCR results. Different DNA extraction and purification methods were compared for quantification of Roundup Ready (RR) soybean (event 40-3-2) by real-time PCR. DNA was extracted using cetylmethylammonium bromide (CTAB), DNeasy Plant Mini Kit, and Wizard Magnetic DNA purification system for food. CTAB-extracted DNA was also purified using the Zymo (DNA Clean & Concentrator 25 kit), Qtip 100 (Qiagen Genomic-Tip 100/G), and QIAEX II Gel Extraction Kit. The CTAB extraction method provided the largest amount of DNA, and the Zymo purification kit resulted in the highest percentage of DNA recovery. The Abs260/280 and Abs260/230 ratios were less than the expected values for some of the DNA extraction and purification methods used, indicating the presence of substances that could inhibit PCR reactions. Real-time quantitative PCR results were affected by the DNA extraction and purification methods used. Further purification or dilution of the CTAB DNA was required for successful quantification of RR soybean. Less variability of quantitative PCR results was observed among experiments and replications for DNA extracted and/or purified by CTAB, CTAB+Zymo, CTAB+Qtip 100, and DNeasy methods. Correct and repeatable results for real-time PCR quantification of RR soybean were achieved using CTAB DNA purified with Zymo and Qtip 100 methods.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".