Efficient DNA Fingerprinting Based on the Targeted Sequencing of Active Retrotransposon Insertion Sites Using a Bench-Top High-Throughput Sequencing Platform
Bibliographic record
Abstract
In many crop species, DNA fingerprinting is required for the precise identification of cultivars to protect the rights of breeders. Many families of retrotransposons have multiple copies throughout the eukaryotic genome and their integrated copies are inherited genetically. Thus, their insertion polymorphisms among cultivars are useful for DNA fingerprinting. In this study, we conducted a DNA fingerprinting based on the insertion polymorphisms of active retrotransposon families (Rtsp-1 and LIb) in sweet potato. Using 38 cultivars, we identified 2,024 insertion sites in the two families with an Illumina MiSeq sequencing platform. Of these insertion sites, 91.4% appeared to be polymorphic among the cultivars and 376 cultivar-specific insertion sites were identified, which were converted directly into cultivar-specific sequence-characterized amplified region (SCAR) markers. A phylogenetic tree was constructed using these insertion sites, which corresponded well with known pedigree information, thereby indicating their suitability for genetic diversity studies. Thus, the genome-wide comparative analysis of active retrotransposon insertion sites using the bench-top MiSeq sequencing platform is highly effective for DNA fingerprinting without any requirement for whole genome sequence information. This approach may facilitate the development of practical polymerase chain reaction-based cultivar diagnostic system and could also be applied to the determination of genetic relationships.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".