Comparative Genomics Analysis Reveals High Levels of Differential Retrotransposition among Primates from the Hominidae and the Cercopithecidae Families
Bibliographic record
Abstract
Mobile elements (MEs), making ∼50% of primate genomes, are known to be responsible for generating inter- and intra-species genomic variations and play important roles in genome evolution and gene function. Using a bioinformatics comparative genomics approach, we performed analyses of species-specific MEs (SS-MEs) in eight primate genomes from the families of Hominidae and Cercopithecidae, focusing on retrotransposons. We identified a total of 230,855 SS-MEs, with which we performed normalization based on evolutionary distances, and we also analyzed the most recent SS-MEs in these genomes. Comparative analysis of SS-MEs reveals striking differences in ME transposition among these primate genomes. Interesting highlights of our results include: 1) the baboon genome has the highest number of SS-MEs with a strong bias for SINEs, while the crab-eating macaque genome has a sustained extremely low transposition for all ME classes, suggesting the existence of a genome-wide mechanism suppressing ME transposition; 2) while SS-SINEs represent the dominant class in general, the orangutan genome stands out by having SS-LINEs as the dominant class; 3) the human genome stands out among the eight genomes by having the largest number of recent highly active ME subfamilies, suggesting a greater impact of ME transposition on its recent evolution; and 4) at least 33% of the SS-MEs locate to genic regions, including protein coding regions, presenting significant potentials for impacting gene function. Our study, as the first of its kind, demonstrates that mobile elements evolve quite differently among these primates, suggesting differential ME transposition as an important mechanism in primate evolution.
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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 | 0.000 |
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".