Sequence analysis of trinucleotide repeat microsatellites from an enrichment library of the equine genome
Bibliographic record
Abstract
Microsatellites are useful tools for the construction of a linkage map and parentage testing of equines, but only a limited number of equine microsatellites have been elucidated. Thus, we constructed the equine genomic library enriched for DNA fragments containing (CAG)n repeats. The enriched method includes hybridization-capture of repeat regions using biotin-conjugated oligonucleotides, nucleotide substrate-biased polymerase reaction with the oligonucleotides and subsequent PCR amplification, because these procedures are useful for the cloning of less abundant trinucleotide microsatellites. Microsatellites containing (CAG)n repeats were obtained at the ratio of one per 3-4 clones, indicating an enrichment value about 10(4)-fold, resulting in less time consumption and less cost for cloning. In this study, 66 different microsatellites, (CAG)n repeats, were identified. The number of complete simple CAG repeats in our clones ranged 4-33, with an average repeat length of 8.8 units. The microsatellites were useful as sequence-tagged site (STS) markers. In addition, some clones containing (CAG)n repeats showed homology to human (CAG)n-containing genes, which have been previously mapped. These results indicate that the clones might be a useful tool for chromosome comparison between equines and humans.
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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 | 0.001 |
| 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) | 0.001 | 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".