Effect of endogenous reference genes on digital PCR assessment of genetically engineered canola events
Bibliographic record
Abstract
Droplet digital PCR (ddPCR) has been used for absolute quantification of genetically engineered (GE) events. Absolute quantification of GE events by duplex ddPCR requires the use of appropriate primers and probes for target and reference gene sequences in order to accurately determine the amount of GE materials. Single copy reference genes are generally preferred for absolute quantification of GE events by ddPCR. Study has not been conducted on a comparison of reference genes for absolute quantification of GE canola events by ddPCR. The suitability of four endogenous reference sequences (HMG-I/Y, FatA(A), CruA and Ccf) for absolute quantification of GE canola events by ddPCR was investigated. The effect of DNA extraction methods and DNA quality on the assessment of reference gene copy numbers was also investigated. ddPCR results were affected by the use of single vs. two copy reference genes. The single copy, FatA(A), reference gene was found to be stable and suitable for absolute quantification of GE canola events by ddPCR. For the copy numbers measured, the HMG-I/Y reference gene was less consistent than FatA(A) reference gene. The expected ddPCR values were underestimated when CruA and Ccf (two copy endogenous Cruciferin sequences) were used because of high number of copies. It is important to make an adjustment if two copy reference genes are used for ddPCR in order to obtain accurate results. On the other hand, real-time quantitative PCR results were not affected by the use of single vs. two copy reference genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".