SNP marker development in Pinus sylvestris L. in stress-responsive genes characterized from Pinus cembra L. transcriptomes
Bibliographic record
Abstract
Massively parallel sequencing of cDNA is an efficient route for generating sequence collections that represent expressed genes under different environmental control. The analysis of their sequence helps in developing molecular markers, such as SNPs, which represent a useful tool in detecting adaptive signals in populations. In this study novel PCR markers, based on stress responsive genes, were designed from the transcriptome of the haploxylon Swiss stone pine (Pinus cembra L.) and tested for SNPs in the diploxylon Scots pine (Pinus sylvestris L.). 84 primers were tested on P. sylvestris DNA samples originating from three different types of habitat. After sequencing and BLAST search of the amplified products, parts of 19 different candidate genes were analysed by considering the polymorphic sites, insertions/deletions as well as synonymous and non-synonymous SNPs. In a total of 3735 sites no indels, eight synonymous and 11 non-synonymous SNPs were found. By providing de novo molecular markers developed in P. cembra and tested for transferability in Scots pine, our results give support for the use of de novo markers targeting conserved regions across different pines. The SNPs detected may have important applications in further studies of adaptive genetic variation, providing tools to study relevant genes important in the long-term adaptation of pine species.
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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 | 0.000 |
| 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) | 0.001 | 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".