Karyotyping in Melon (Cucumis melo L.) by Cross-Species Fosmid Fluorescence in situ Hybridization
Bibliographic record
Abstract
Chromosome identification is critical for cytogenetic research and will accelerate studies on genetic variation and breeding, especially for those species with relatively little sequence information. So far, no reliable cytological landmarks have been developed to distinguish individual chromosomes in melon. In this study, using FISH (fluorescence in situ hybridization) combined with comparative genome information, we selected 21 cucumber fosmids anchored by SSR markers as chromosome-specific cytological markers for melon chromosomes. Moreover, with the help of melon centromeric satellite DNA repeats CentM, 45S rDNA and 5S rDNA, sequential FISH with 3 sets of multi-fosmid cocktails were conducted on the same metaphase cell, which allowed us to simultaneously identify each of the 12 metaphase chromosomes of melon and a standardized melon karyotype of somatic metaphase chromosomes was constructed. Finally, we compared the distribution of 21 FISH-mapped fosmids between melon and cucumber chromosomes, which allows a better understanding of the evolutionary process shaping these 2 species. Our study provides a basis for cytological characterization of the melon genome and comparative genomics of Cucurbitaceae.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".