Microsatellite landscape evolutionary dynamics across 450 million years of vertebrate genome evolution
Bibliographic record
Abstract
The evolutionary dynamics of simple sequence repeats (SSRs or microsatellites) across the vertebrate tree of life remain largely undocumented and poorly understood. In this study, we analyzed patterns of genomic microsatellite abundance and evolution across 71 vertebrate genomes. The highest abundances of microsatellites exist in the genomes of ray-finned fishes, squamate reptiles, and mammals, while crocodilian, turtle, and avian genomes exhibit reduced microsatellite landscapes. We used comparative methods to infer evolutionary rates of change in microsatellite abundance across vertebrates and to highlight particular lineages that have experienced unusually high or low rates of change in genomic microsatellite abundance. Overall, most variation in microsatellite content, abundance, and evolutionary rate is observed among major lineages of reptiles, yet we found that several deeply divergent clades (i.e., squamate reptiles and mammals) contained relatively similar genomic microsatellite compositions. Archosauromorph reptiles (turtles, crocodilians, and birds) exhibit reduced genomic microsatellite content and the slowest rates of microsatellite evolution, in contrast to squamate reptile genomes that have among the highest rates of microsatellite evolution. Substantial branch-specific shifts in SSR content in primates, monotremes, rodents, snakes, and fish are also evident. Collectively, our results support multiple major shifts in microsatellite genomic landscapes among vertebrates.
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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 | 0.001 | 0.001 |
| 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".