Linking porcine microsatellite markers to known genome regions by identifying their human orthologs
Bibliographic record
Abstract
Microsatellites, or tandem simple sequence repeats (SSRs), have become one of the most popular molecular markers in genome mapping because of their abundance across genomes and because of their high levels of polymorphism. However, information on which genes surround or flank them has remained very limited for most SSRs, especially in livestock species. In this study, an in silico comparative mapping approach was developed to link porcine SSRs to known genome regions by identifying their human orthologs. From a total of 1321 porcine microsatellites used in this study, 228 were found to have blocks in alignment with human genomic sequences. These 228 SSRs span about 1459 cM of the porcine genome, but with uneven distributions, ranging from 2 on SSC12 to 24 on SSC14. Linking these porcine SSRs to the known genome regions in the human genome also revealed 16 new putative synteny groups between these two species. Fifteen SSRs on SSC3 with identified human orthologs were typed on a pig-hamster radiation hybrid (RH) panel and used in a joint analysis with 80 known gene markers previously mapped on SSC3 using the same panel. The analysis revealed that they were all highly linked to either one or both adjacent markers. These results indicated that assigning the porcine SSRs to known genome regions by identifying their human orthologs is a reliable approach. The process will provide a foundation for positional cloning of causative genes for economically important traits.
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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 | 0.001 | 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) | 0.002 | 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".