SSR Molecular Markers Development Based on Whole Genome Sequences in Glycyrrhiza uralensis Fisch.
Bibliographic record
Abstract
It is different in terms of active ingredients from various sources and origins on Glycyrrhiza Linn.. The development of specific molecular markers is of great significance for identification of germplasm resources. In the study, simple sequence repeat (SSR) motifs were analyzed based on the whole genome data in Glycyrrhiza uralensis Fisch.. SSRs were identified with MISA tool in all scaffold sequences. Suitable SSRs were used to design primers by using Primer3-2.4.0 software and 100 pairs of SSR primers were randomly utilized for the validity of molecular markers. The results showed 193 207 SSRs were detected from the whole genome sequence of G. uralensis distributed in 9 250 scaffolds. Of these, mono-nucleotide repeat SSRs was the main type, accounting for 60.73%, followed by dinucleotide (26.11%) and trinucleotide repeat (10.95%). Of the 284 repeat motifs, A/T (58.28%), AG/CT (10.48%), AT/AT (10.48%), AC/GT (5.12%) and AAT/ATT (3.57%) were the main repeat base. In addition, SSR primers for 140294 SSRs were designed using Primer3-2.4.0 software. We eventually developed 701 302 pairs of SSR markers in G. uralensis. Subsequently, 100 pairs of SSR primers were randomly selected for the further verifying. PCR results indicated that 63 of them (63%) had amplified the target bands, and 12 pairs of primers could generate polymorphic bands in seven samples. A large number of SSR markers developed will be used for identification of Glycyrrhiza germplasm resources and future molecular marker assisted breeding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".