Conservation of Human Microsatellites across 450 Million Years of Evolution
Bibliographic record
Abstract
The sequencing and comparison of vertebrate genomes have enabled the identification of widely conserved genomic elements. Chief among these are genes and cis-regulatory regions, which are often under selective constraints that promote their retention in related organisms. The conservation of elements that either lack function or whose functions are yet to be ascribed has been relatively little investigated. In particular, microsatellites, a class of highly polymorphic repetitive sequences considered by most to be neutrally evolving junk DNA that is too labile to be maintained in distant species, have not been comprehensively studied in a comparative genomic framework. Here, we used the UCSC alignment of the human genome against those of 11 mammalian and five nonmammalian vertebrates to identify and examine the extent of conservation of human microsatellites in vertebrate genomes. Out of 696,016 microsatellites found in human sequences, 85.39% were conserved in at least one other species, whereas 28.65% and 5.98% were found in at least one and three nonprimate species, respectively. An exponential decline of microsatellite conservation with increasing evolutionary time, a comparable distribution of conserved versus nonconserved microsatellites in the human genome, and a positive correlation between microsatellite conservation and overall sequence conservation, all suggest that most microsatellites are only maintained in genomes by chance, although exceptionally conserved human microsatellites were also found in distant mammals and other vertebrates. Our findings provide the first comprehensive survey of microsatellite conservation across deep evolutionary timescales, in this case 450 Myr of vertebrate evolution, and provide new tools for the identification of functional conserved microsatellites, the development of cross-species microsatellite markers and the study of microsatellite evolution above the species level.
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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) | 0.000 | 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".