Successful detection of foreign inserts in transgenic rice TT51‐1 (BT63) by RNA‐sequencing combined with PCR
Bibliographic record
Abstract
BACKGROUND: As event-specific sequence information for most unauthorised genetically modified organisms (GMOs) is currently still unavailable, detecting unauthorised GMOs remains challenging. Here, we used insect-resistant rice TT51-1 as an example to develop a novel approach via detecting GMOs by RNA-seq (sequencing) and PCR. RNA-seq of TT51-1 generated 4.8 million (M) 21-nt cDNA tags. Alignment to the Oryza sativa subsp. japonica reference genome revealed 24 098 unmapped tags. Foreign tags from the nopaline synthetic enzyme gene (NOS) terminator and insect-resistant genes were then identified by searching against the NCBI VecScreen and NT databases. RESULTS: To further isolate foreign DNA sequences, putative NOS terminator and insect-resistant gene tags were combined and used directly as primer pairs for long-range PCR, producing a 5016-bp fragment. The inserted DNA sequence of TT51-1 has been submitted to a database, and thus, similarity analysis using the database could identify a test sample. CONCLUSION: The novel approach has a great potential for application to the detection and identification of unauthorised GMOs in food and feed products. © 2016 Society of Chemical Industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".