Sequence, Structural and Expression Divergence of Duplicate Genes in the Bovine Genome
Bibliographic record
Abstract
Gene duplication is a widespread phenomenon in genome evolution, and it has been proposed to serve as an engine of evolutionary innovation. In the present study, we performed the first comprehensive analysis of duplicate genes in the bovine genome. A total of 3131 putative duplicated gene pairs were identified, including 712 cattle-specific duplicate gene pairs unevenly distributed across the genome, which are significantly enriched for specific biological functions including immunity, growth, digestion, reproduction, embryonic development, inflammatory response, and defense response to bacterium. Around 97.1% (87.8%) of (cattle-specific) duplicate gene pairs were found to have distinct exon-intron structures. Analysis of gene expression by RNA-Seq and sequence divergence (synonymous or non-synonymous) revealed that expression divergence is correlated with sequence divergence, as has been previously observed in other species. This analysis also led to the identification of a subset of cattle-specific duplicate gene pairs exhibiting very high expression divergence. Interestingly, further investigation revealed a significant relationship between structural and expression divergence while controlling for the effect of synonymous sequence divergence. Together these results provide further insight into duplicate gene sequence and expression divergence in cattle, and their potential contributions to phenotypic divergence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".