A highly contiguous genome assembly of red perilla ( <i>Perilla frutescens</i> ) domesticated in Japan
Bibliographic record
Abstract
Perilla frutescens (Lamiaceae) is an important herbal plant with hundreds of bioactive chemicals, among which perillaldehyde and rosmarinic acid are the two major bioactive compounds in the plant. The leaves of red perilla are used as traditional Kampo medicine or food ingredients. However, the medicinal and nutritional uses of this plant could be improved by enhancing the production of valuable metabolites through the manipulation of key enzymes or regulatory genes using genome editing technology. Here, we generated a high-quality genome assembly of red perilla domesticated in Japan. A near-complete chromosome-level assembly of P. frutescens was generated contigs with N50 of 41.5 Mb from PacBio HiFi reads. 99.2% of the assembly was anchored into 20 pseudochromosomes, among which seven pseudochromosomes consisted of one contig, while the rest consisted of less than six contigs. Gene annotation and prediction of the sequences successfully predicted 86,258 gene models, including 76,825 protein-coding genes. Further analysis showed that potential targets of genome editing for the engineering of anthocyanin pathways in P. frutescens are located on the late-stage pathways. Overall, our genome assembly could serve as a valuable reference for selecting target genes for genome editing of P. frutescens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".