Hybrid Genome Assembly of <i>Berkeleyomyces rouxiae</i>, an Emerging <i>Cannabis</i> Fungal Pathogen Causing Black Root Rot in an Aeroponic Facility
Bibliographic record
Abstract
The resurged interest in cultivation of Cannabis sativa has presented an array of new challenges. Among them are the difficult-to-control pests and pathogens that infect cannabis plants. The limited methods for disease control available to cannabis growers necessitates early detection of plant pathogens, something that molecular techniques such as DNA sequencing has greatly improved. This study reports for the first time the fungal plant pathogen Berkeleyomyces rouxiae causing black root rot in high THC-containing cannabis. Aeroponically grown cannabis plants at a licenced production facility in Cranbrook BC, Canada, rapidly displayed root discoloration and rot symptoms despite testing negative for all commercially available pathogen tests. Developing sequencing-based disease diagnostics requires genomic information, so this study presents the first whole genome sequence of the multihost, widespread black root rot pathogen B. rouxiae. Hybrid genome assembly using Oxford Nanopore long-reads and Illumina short-reads yielded a genome size of 28.2 Mb represented over 404 contigs with an N50 of 267 kb. Genome annotation predicted 6,960 protein-coding genes with 59,477 functional annotations. The availability of this genome will assist in sequence-based diagnostic development, comparative genomics, and taxonomic resolution of this globally important plant pathogen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".