A chromosome‐scale draft sequence of the Canada fleabane genome
Bibliographic record
Abstract
BACKGROUND: Due to the accessibility of underlying technologies the 'Omics', in particular genomics, are becoming commonplace in several fields of research, including the study of agricultural pests. The weed community is starting to embrace these approaches; genome sequences have been made available in the past years, with several other sequencing projects underway, as promoted by the International Weed Genome Consortium. Chromosome-scale sequences are essential to fully exploit the power of genetics and genomics. RESULTS: We report such an assembly for Conyza canadensis, an important agricultural weed. Third-generation sequencing technology was used to create a genome assembly of 426 megabases, of which nine chromosome-scale scaffolds cover more than 98% of the entire assembled sequence. As this weed was the first to be identified with glyphosate resistance, and since we do not have a firm handle on the genetic mechanisms responsible for several herbicide resistances in the species, the genome sequence was annotated with genes known to be associated with herbicide resistance. A high number of ABC-type transporters, cytochrome P450 and glycosyltransferases (159, 352 and 181, respectively) were identified among the list of ab initio predicted genes. CONCLUSION: As C. canadensis has a small genome that is syntenic with other Asteraceaes, has a short life cycle and is relatively easy to cross, it has the potential to become a model weed species and, with the chromosome-scale genome sequence, contribute to a paradigm shift in the way non-target site resistance is studied. © 2020 Her Majesty the Queen in Right of CanadaPest Management Science © 2020 Society of Chemical Industry.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".