The Development of EST-SSR Markers from Transcriptome Sequencing and Genetic Diversity of Twenty Genotypes with High Yields of <i>Cornus wilsoniana</i>, an Important Wood Oil Plant
Bibliographic record
Abstract
Oil plants are not only used as essential nutriments in food but also used broadly as ingredients of industrial products. Cornus wilsoniana Wanger is an important native woody oil plant with high-yield and high-oiliness characters in China. The fruit oil of Cornus wilsoniana not only could be used as equilibrated dietary oil, but also has hypolipidemic function and helps to overcome EFA deficiency. Genomic information is currently not available for Cornus wilsoniana , which will therefore affect its genetic improvement process. In this study, 8713 EST-SSRs were identified from the transcriptome sequencing of Cornus wilsoniana . Most of these EST-SSRs were composed of dimer and trimer repeats. The AG/CT motif is the most common dimeric EST-SSRs motif (53.5%), whereas the CG/CG (0.44%) microsatellites are present only at low abundances. Among the trimeric microsatellites, AAG/CTT (5.53%) and ATC/ATG (4.27%) are the most common motifs. There are no obvious dominant motifs among the tetra-, penta-, and hexa- nucleotide motifs. Fifteen pairs of EST-SSRs primers are developed with polymorphism and used for the genetic diversity analysis of twenty genotypes of Cornus wilsoniana with high fruit output. Allele number per locus ranges from 2 to6 with an average of 2.87, and the PIC value ranges from 0.05 to 0.58 with an average of 0.36. Average genetic diversity overall SSR loci for the 20 genotypes was 0.427, ranging from 0.049 to 0.659. All the loci are polymorphic and clearly distinguish the genotypes. Cluster analyze (NJ tree, UPGMA) identifies a similar pattern of variation. 15developed EST-SSRs are informative, codominant and reliable, and could be applied in the improvement programs of Cornus wilsoniana in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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) | 0.001 | 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".