The draft genome and transcriptome of Cannabis sativa
Bibliographic record
Abstract
BACKGROUND: Cannabis sativa has been cultivated throughout human history as a source of fiber, oil and food, and for its medicinal and intoxicating properties. Selective breeding has produced cannabis plants for specific uses, including high-potency marijuana strains and hemp cultivars for fiber and seed production. The molecular biology underlying cannabinoid biosynthesis and other traits of interest is largely unexplored. RESULTS: We sequenced genomic DNA and RNA from the marijuana strain Purple Kush using shortread approaches. We report a draft haploid genome sequence of 534 Mb and a transcriptome of 30,000 genes. Comparison of the transcriptome of Purple Kush with that of the hemp cultivar 'Finola' revealed that many genes encoding proteins involved in cannabinoid and precursor pathways are more highly expressed in Purple Kush than in 'Finola'. The exclusive occurrence of Δ9-tetrahydrocannabinolic acid synthase in the Purple Kush transcriptome, and its replacement by cannabidiolic acid synthase in 'Finola', may explain why the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC) is produced in marijuana but not in hemp. Resequencing the hemp cultivars 'Finola' and 'USO-31' showed little difference in gene copy numbers of cannabinoid pathway enzymes. However, single nucleotide variant analysis uncovered a relatively high level of variation among four cannabis types, and supported a separation of marijuana and hemp. CONCLUSIONS: The availability of the Cannabis sativa genome enables the study of a multifunctional plant that occupies a unique role in human culture. Its availability will aid the development of therapeutic marijuana strains with tailored cannabinoid profiles and provide a basis for the breeding of hemp with improved agronomic characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".