Characterization and comparison of microsatellites derived from repeat‐enriched libraries and expressed sequence tags
Bibliographic record
Abstract
The construction of high-density linkage maps for use in identifying loci underlying important traits requires the development of large numbers of polymorphic genetic markers spanning the entire genome at regularly spaced intervals. As part of our efforts to develop markers for rainbow trout (Oncorhynchus mykiss), we performed a comparison of allelic variation between microsatellite markers developed from expressed sequence tag (EST) data and anonymous markers identified from repeat-enriched libraries constructed from genomic DNA. A subset of 70 markers (37 from EST databases and 33 from repeat enriched libraries) was characterized with respect to polymorphism information content (PIC), number of alleles, repeat number, locus duplication within the genome and ability to amplify in other salmonid species. Higher PIC was detected in dinucleotide microsatellites derived from ESTs than anonymous markers (72.7% vs. 54.0%). In contrast, dinucleotide repeat numbers were higher for anonymous microsatellites than for EST derived microsatellites (27.4 vs.18.1). A higher rate of cross-species amplification was observed for EST microsatellites. Approximately half of each marker type was duplicated within the genome. Unlike single-copy markers, amplification of duplicated microsatellites in other salmonids was not correlated to phylogenetic distance. Genomic microsatellites proved more useful than EST derived microsatellites in discriminating among the salmonids. In total, 428 microsatellite markers were developed in this study for mapping and population genetic studies in rainbow trout.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".