Development and characterization of Novel EST-based single-copy genic microsatellite DNA markers in white spruce and black spruce
Bibliographic record
Abstract
Abstract Background Due mainly to large genome size and prevalence of repetitive sequences in the nuclear genome of spruce ( Picea ), it is very difficult to develop single-copy genomic microsatellite markers. We have developed and characterized 25 polymorphic, single-copy genic microsatellites from white spruce ( Picea glauca ) EST sequences and determined their informativeness in white spruce and black spruce ( Picea mariana ) and inheritance in black spruce. Methods and Results White spruce EST sequences from NCBI dbEST were searched for the presence of microsatellite repeats. Forty-seven sequences containing dinucleotide, trinucleotide, tetranucleotide and compound repeats were selected to develop primers. Twenty-five of the designed primer pairs yielded scorable amplicons, with single-locus patterns, and were characterized in 20 individuals each of white spruce and black spruce. All 25 microsatellites were polymorphic in white spruce and 24 in black spruce. The number of alleles at a locus ranged from 2 to 18, with a mean of 8.8 in white spruce, and from 1 to 17, with a mean of 7.6 in black spruce. The expected heterozygosity/polymorphic information content ranged from 0.10 to 0.92, with a mean of 0.67 in white spruce, and from 0 to 0.93, with a mean of 0.59 in black spruce. Conclusions Microsatellites with dinucleotide and compound repeats were more informative than those with trinucleotide and tetranucleotide repeats. Eighteen microsatellite markers polymorphic between the parents of a black spruce controlled cross inherited in a single-locus Mendelian fashion. The microsatellite markers developed can be applied for various genetics, genomics, breeding, and conservation studies and applications.
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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 | 0.000 | 0.000 |
| 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".