Full-length transcriptome analysis and identification of genes involved in asarinin and aristolochic acid biosynthesis in medicinal plant <i>Asarum sieboldii</i>
Bibliographic record
Abstract
Asarum sieboldii, a well-known traditional Chinese medicinal herb, is used for curing inflammation and ache. It contains both the bioactive ingredient asarinin and the toxic compound aristolochic acid. To address further breeding demand, genes involved in the biosynthetic pathways of asarinin and aristolochic acid should be explored. Therefore, the full-length transcriptome of A. sieboldii was sequenced using PacBio Iso-Seq to determine the candidate transcripts that encode the biosynthetic enzymes of asarinin and aristolochic acid. In this study, 63 023 full-length transcripts were generated with an average length of 1371 bp from roots, stems, and leaves, of which 49 593 transcripts (78.69%) were annotated against public databases. Furthermore, 555 alternative splicing (AS) events, 10 869 long noncoding RNAs (lncRNAs) as well as their 11 291 target genes, and 17 909 simple sequence repeats (SSRs) were identified. The data also revealed 97 candidate transcripts related to asarinin metabolism, of which six novel genes that encoded enzymes involved in asarinin biosynthesis were initially reported. In addition, 56 transcripts related to aristolochic acid biosynthesis were also identified, including CYP81B. In summary, these transcriptome data provide a useful resource to study gene function and genetic engineering in A. sieboldii.
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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 | 0.001 |
| 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 | 0.000 |
| 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".