Transcriptome-wide characterization of alternative splicing in five drug-type cultivars of <i>Cannabis sativa</i>
Bibliographic record
Abstract
Cannabis sativa L. is widely used for fiber, medicinal, and other purposes, and many cultivars exist, yielding varying proportions of cannabinoids and terpenes. There is considerable interest in characterizing genomes and transcriptomes of C. sativa. Alternative splicing (AS) is a fundamental aspect of gene expression that results in multiple types of mRNAs produced by differential splicing. Transcriptome-wide identification of AS events in drug-type cultivars of C. sativa has not been reported. Here, we identified AS events using a transcriptome dataset derived from five drug-type cultivars with divergent chemotypes. Intron retention is the most common event type, followed by alternative acceptor, alternative donor, and skipped exons. We also sought to assess conservation of AS events among cultivars. We found 547 events (5%) unique to a single cultivar, 2661 (25%) shared by 2–4 cultivars, and 7569 (70%) common to all 5 cultivars. Genes with AS events in each set were analyzed for gene ontology enrichment, showing that genes with AS unique to a single cultivar are enriched for molecular functions related to interactions with ATP and processes involving transport within cells and across membranes. These results provide insights into the conservation and variation of AS events in multiple cultivars of C. sativa.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".