Development of retrotransposons insertion polymorphic markers and application in the genetic variation evaluation of Chinese Bama miniature pigs
Post-publication record
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Bibliographic record
Abstract
Retrotransposons are genetic elements that can amplify themselves in a genome and are abundant in many eukaryotic organisms. In this study, we established some new short interspersed nuclear elements (SINE) and endogenous retroviruses (ERV) retrotransposons insertion polymorphism (RTIP) markers based on BLAT alignment tool strategy, and followed by PCR evaluation. We investigated the genetic variations among four subpopulations of Chinese Bama miniature pigs (BM), including BM in national conservation farm (BM-cov), BM inbreeding population (BM-inb) and BM closed Herd (BM-clo) in Guangxi University, and BM in the Experimental pig farm of Yangzhou University (BM-yzu). Genetic distance, polymorphism information content (PIC) and heterozygosity (He) of these markers in four of BM subpopulations were measured. Twelve SINE and twenty-eight ERV polymorphic molecular markers were identified in the four subpopulations. The BM-cov pigs represented the highest He and PIC, which indicated that BM-cov pigs maintain relatively highly genetic diversity. BM-inb pigs represented the lowest He and PIC indicating less variation and a high degree of inbreeding. Microsatellites polymorphism in four BM populations also well supported the results of these RTIP markers. In summary, retrotransposons insertion polymorphic markers could be a useful tool for population genetic variation analysis. Current SINE and ERV variation data may also provide a reference guide for the conservation and utilization of the BM miniature pig resource.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".