Measuring conservation of contiguous sets of autosomal markers on bovine and porcine genomes in relation to the map of the human genome
Bibliographic record
Abstract
Based on published information, we have identified 991 genes and gene-family clusters for cattle and 764 for pigs that have orthologues in the human genome. The relative linear locations of these genes on human sequence maps were used as "rulers" to annotate bovine and porcine genomes based on a CSAM (contiguous sets of autosomal markers) approach. A CSAM is an uninterrupted set of markers in one genome (primary genome; the human genome in this study) that is syntenic in the other genome (secondary genome; the bovine and porcine genomes in this study). The analysis revealed 81 conserved syntenies and 161 CSAMs between human and bovine autosomes and 50 conserved syntenies and 95 CSAMs between human and porcine autosomes. Using the human sequence map as a reference, these 991 and 764 markers could correlate 72 and 74% of the human genome with the bovine and porcine genomes, respectively. Based on the number of contiguous markers in each CSAM, we classified these CSAMs into five size groups as follows: singletons (one marker only), small (2-4 markers), medium (5-10 markers), large (11-20 markers), and very large (> 20 markers). Several bovine and porcine chromosomes appear to be represented as di-CSAM repeats in a tandem or dispersed way on human chromosomes. The number of potential CSAMs for which no markers are currently available were estimated to be 63 between human and bovine genomes and 18 between human and porcine genomes. These results provide basic guidelines for further gene and QTL mapping of the bovine and porcine genomes, as well as insight into the evolution of mammalian genomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".