Development of SSR markers via de novo transcriptome assembly in <i>Akebia trifoliata</i> (Thunb.) Koidz
Bibliographic record
Abstract
Owing to its high nutritive, economic, and medicinal values, Akebia trifoliata has received increased attention, making worthy of being used as a new fruit crop for further domestication and commercialization in China. However, molecular research of A. trifoliata has lagged as investigations of its genomic resources and molecular markers are rare. In this study, a cDNA library of A. trifoliata leaves was sequenced using the Illumina NovaSeq. 6000 sequencing system. In total, 101 417 transcripts, 63 757 unigenes, and 9494 simple sequence repeats were assembled and identified from the transcriptome datasets. The majority of the SSRs were di- and trinucleotide repeats. Length and number of SSR motifs ranged from 15 to 66, and 5 to 48 bp, respectively. Of which, the A/T mononucleotide motif and AG/TC and CT/GA dinucleotide motifs were the most abundant. Furthermore, 100 SSR primers were randomly selected to validate amplification and polymorphism, and 88 A. trifoliata accessions were definitively distinguished by 49 primers. With the Qinling mountains and Huaihe River line as the boundaries, the northern and southern accessions were clustered into different groups, but no clear geographical patterns (city or origin) were observed in the southern accessions. These newly identified molecular markers may provide a foundation for the genetic identification and diversity analysis and marker-assisted selection breeding in species of Akebia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".