Increasing the physical markers of wheat chromosomes using SSRs as FISH probes
Bibliographic record
Abstract
In plants the marker sequences used to identify chromosomes are mainly repetitive DNA probes. Simple sequence repeats (SSRs) are major components of many plant genomes and could be good markers for chromosome identification. In a previous work, we reported the physical distribution of 4 oligonucleotides, (AG)12, (CAT)5, (AAC)5, and (AAG)5, on Triticum aestivum L. chromosomes. The distinctive distribution pattern found suggested that SSR in situ hybridization is useful as a diagnostic tool in wheat cytogenetics. To check whether that finding is generally applicable, we analyzed the chromosomal distribution of the rest of the 14 possible classes of di- and tri-nucleotide repeats by FISH. A detailed knowledge of the sequence content of hexaploid wheat chromatin was acquired based on the hybridization signals, which also provide a rich set of chromosome markers for chromosome identification. Except for (AT)10 and (GC)10, for which the chromosomal distribution could not be accurately determined, and (AC)8 and (GCC)5, which were found dispersed throughout the chromosomes, the remaining repeats were observed as clusters on specific chromosome sites. (AGG)5, (CAC)5, (ACG)5, (AAT)5, and (CAG)5 exhibited a preferential distribution in the pericentromeric regions of the B genome chromosomes. The richest patterns of intercalary signals on several A and B genome chromosomes were produced by (ACT)5. A karyotype based on the SSR probes providing the best FISH patterns was constructed for T. aestivum 'Chinese Spring'.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".