Sequencing and annotation of the Ophiostoma ulmigenome
Bibliographic record
Abstract
BACKGROUND: The ascomycete fungus Ophiostoma ulmi was responsible for the initial pandemic of the massively destructive Dutch elm disease in Europe and North America in early 1910. Dutch elm disease has ravaged the elm tree population globally and is a major threat to the remaining elm population. O. ulmi is also associated with valuable biomaterials applications. It was recently discovered that proteins from O. ulmi can be used for efficient transformation of amylose in the production of bioplastics. RESULTS: We have sequenced the 31.5 Mb genome of O.ulmi using Illumina next generation sequencing. Applying both de novo and comparative genome annotation methods, we predict a total of 8639 gene models. The quality of the predicted genes was validated using a variety of data sources consisting of EST data, mRNA-seq data and orthologs from related fungal species. Sequence-based computational methods were used to identify candidate virulence-related genes. Metabolic pathways were reconstructed and highlight specific enzymes that may play a role in virulence. CONCLUSIONS: This genome sequence will be a useful resource for further research aimed at understanding the molecular mechanisms of pathogenicity by O. ulmi. It will also facilitate the identification of enzymes necessary for industrial biotransformation applications.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".