Foreign DNA detection in genome-edited potatoes by high-throughput sequencing
Bibliographic record
Abstract
Genome editing is a powerful breeding technique that introduces mutations into specific gene sequences in genomes. For genome editing in higher plants, nucleotides for artificial nuclease (e.g. TALEN or CRISPR-Cas9) are transiently or stably introduced into the plant cells. After the introduction of mutations by artificial nucleases, it is necessary to select lines that do not contain the foreign nucleotides to overcome GMO regulation; however, there is still no widely legally authorized and approved method for detecting foreign genes in genome-edited crops. Recently, k-mer analysis based on next-generation sequencing (NGS) was proposed as a new method for detecting foreign DNA in genome-edited agricultural products. Compared to conventional methods, such as PCR and Southern hybridization, in principle, this method can detect short DNA fragments with high accuracy. However, this method has not yet been applied to genome-edited potatoes. In this study, we evaluated the feasibility of k-mer analysis in tetraploid potatoes by computer simulation, and also evaluated whether the k-mer method can detect foreign genes with high accuracy by analyzing samples of genome-edited potatoes. We show that when NGS data (at a depth of × 30 the genome size) are used, the k-mer method can correctly detect foreign genes in the potato genome even with the insertion of DNA fragments of 20 nt in length. Based on these findings, we expect that k-mer analysis will be one of the main methods for detecting foreign genes in genome-edited potatoes.
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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 | 0.000 | 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 | 0.001 | 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 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".