Undermethylated DNA as a source of microsatellites from a conifer genome
Bibliographic record
Abstract
Developing microsatellites from the large, highly duplicated conifer genome requires special tools. To improve the efficiency of developing Pinus taeda L. microsatellites, undermethylated (UM) DNA fragments were used to construct a microsatellite-enriched copy library. A methylation-sensitive restriction enzyme, McrBC, was used to enrich for UM DNA before library construction. Digested DNA fragments larger than 9 kb were then excised and digested with RsaI and used to construct nine dinucleotide and trinucleotide libraries. A total of 1016 microsatellite-positive clones were detected among 11 904 clones and 620 of these were unique. Of 245 primer sets that produced a PCR product, 113 could be developed as UM microsatellite markers and 70 were polymorphic. Inheritance and marker informativeness were tested for a random sample of 36 polymorphic markers using a three-generation outbred pedigree. Thirty-one microsatellites (86%) had single-locus inheritance despite the highly duplicated nature of the P. taeda genome. Nineteen UM microsatellites had highly informative intercross mating type configurations. Allele number and frequency were estimated for eleven UM microsatellites using a population survey. Allele numbers for these UM microsatellites ranged from 3 to 12 with an average of 5.7 alleles/locus. Frequencies for the 63 alleles were mostly in the low-common range; only 14 of the 63 were in the rare allele (q < 0.05) class. Enriching for UM DNA was an efficient method for developing polymorphic microsatellites from a large plant genome.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".