Identification of chromosomes by fluorescence in situ hybridization in <i>Gossypium hirsutum</i> via developing oligonucleotide probes
Bibliographic record
Abstract
Discrimination of chromosome is essential for chromosome manipulation or visual chromosome characterization. Oligonucleotide probes can be employed to simplify the procedures of chromosome identification in molecular cytogenetics due to its simplicity, fastness, cost-effectiveness, and high efficiency. So far, however, visual identification of cotton chromosomes remains unsolved. Here, we developed 16 oligonucleotide probes for rapid and accurate identification of chromosomes in Gossypium hirsutum: 9 probes, of which each is able to distinguish individually one pair of chromosomes, and seven probes, of which each distinguishes multiple pairs of chromosomes. Besides the identification of Chrs. A09 and D09, we first find Chr. D08, which carries both 45S and 5S rDNA sequences. Interestingly, we also find Chr. A07 has a small 45S rDNA size, suggesting that the size of this site on Chr. A07 may have reduced during evolution. By the combination of 45S and 5S rDNA sequences and oligonucleotide probes developed, 10 chromosomes (Chrs. 3–7, and 9–13) in A subgenome and 7 (Chrs. 1–2, 4–5, and 7–9) in D subgenome of cotton are able to be recognized. This study establishes cotton oligonucleotide fluorescence in situ hybridization technology for discrimination of chromosomes, which supports and guides for sequence assembling, particularly, for tandem repeat sequences in cotton.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".