MICROSATELLITE EVOLUTION IN VERTEBRATES: INFERENCE FROM AC DINUCLEOTIDE REPEATS
Bibliographic record
Abstract
We analyze published data from 592 AC microsatellite loci from 98 species in five vertebrate classes including fish, reptiles, amphibians, birds, and mammals. We use these data to address nine major questions about microsatellite evolution. First, we find that larger genomes do not have more microsatellite loci and therefore reject the hypothesis that microsatellites function primarily to package DNA into chromosomes. Second, we confirm that microsatellite loci are relatively rare in avian genomes, but reject the hypothesis that this is due to physical constraints imposed by flight. Third, we find that microsatellite variation differs among species within classes, possibly relating to population dynamics. Fourth, we reject the hypothesis that microsatellite structure (length, number of alleles, allele dispersion, range in allele sizes) differs between poikilotherms and homeotherms. The difference is found only in fish, which have longer microsatellites and more alleles than the other classes. Fifth, we find that the range in microsatellite allele size at a locus is largely due to the number of alleles and secondarily to allele dispersion. Sixth, length is a major factor influencing mutation rate. Seventh, there is a directional mutation toward an increase in microsatellite length. Eighth, at the species level, microsatellite and allozyme heterozygosity covary and therefore inferences based on large-scale studies of allozyme variation may also reflect microsatellite genetic diversity. Finally, published microsatellite loci (isolated using conventional hybridization methods) provide a biased estimate of the actual mean repeat length of microsatellites in the genome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| 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".