Herbicide targets and detoxification proteins in sugarcane: from gene assembly to structure modelling
Bibliographic record
Abstract
In a genome context, sugarcane is a classic orphan crop, in that no genome and only very few genes have been assembled. We have devised a novel exome assembly methodology that has allowed us to assemble and characterize 49 genes that serve as herbicide targets, safener interacting proteins, and members of herbicide detoxification pathways within the sugarcane genome. We have structurally modelled the products of each of these genes, as well as determining allelic, genomic, and RNA-Seq based polymorphisms for each gene. This study provides the largest collection of sugarcane structures modelled to date. We demonstrate that sugarcane genes are highly polymorphic, revealing that each genotype is evolving both uniquely and independently. In addition, we present an exome assembly system for orphan crops that can be executed on commodity infrastructure, making exome assembly practical for any group. In terms of knowledge about herbicide modes of action and detoxification, we have advanced sugarcane from a crop where no information about any herbicide-associated gene was available to the situation where sugarcane is now a species with the single largest collection of known and annotated herbicide-associated genes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".