Microsatellite analyses across three diverse vertebrate transcriptomes (<i>Acipenser fulvescens</i>,<i>Ambystoma tigrinum</i>, and<i>Dipodomys spectabilis</i>)
Bibliographic record
Abstract
Historically, many population genetics studies have utilized microsatellite markers sampled at random from the genome and presumed to be selectively neutral. Recent studies, however, have shown that microsatellites can occur in transcribed regions, where they are more likely to be under selection. In this study, we mined microsatellites from transcriptomes generated by 454-pyrosequencing for three vertebrate species: lake sturgeon (Acipenser fulvescens), tiger salamander (Ambystoma tigrinum), and kangaroo rat (Dipodomys spectabilis). We evaluated (i) the occurrence of microsatellites across species; (ii) whether particular gene ontology terms were over-represented in genes that contained microsatellites; (iii) whether repeat motifs were located in untranslated regions or coding sequences of genes; and (iv) in silico polymorphism. Microsatellites were less common in tiger salamanders than in either lake sturgeon or kangaroo rats. Across libraries, trinucleotides were found more frequently than any other motif type, presumably because they do not cause frameshift mutations. By evaluating variation across reads assembled to a given contig, we were able to identify repeat motifs likely to be polymorphic. Our study represents one of the first comparative data sets on the distribution of vertebrate microsatellites within expressed genes. Our results reinforce the idea that microsatellites do not always occur in noncoding DNA, but commonly occur in expressed genes.
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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".