Comparative study of genome-wide plant biomass-degrading CAZymes in white rot, brown rot and soft rot fungi
Bibliographic record
Abstract
We have conducted a genome-level comparative study of basidiomycetes wood-rotting fungi (white, brown and soft rot) to understand the total plant biomass (lignin, cellulose, hemicellulose and pectin) -degrading abilities. We have retrieved the genome-level annotations of well-known 14 white rot fungi, 15 brown rot fungi and 13 soft rot fungi. Based on the previous literature and the annotations obtained from CAZy (carbohydrate-active enzyme) database, we have separated the genome-wide CAZymes of the selected fungi into lignin-, cellulose-, hemicellulose- and pectin-degrading enzymes. Results obtained in our study reveal that white rot fungi, especially Pleurotus eryngii and Pleurotus ostreatus potentially possess high ligninolytic ability, and soft rot fungi, especially Botryosphaeria dothidea and Fusarium oxysporum sp., potentially possess high cellulolytic, hemicellulolytic and pectinolytic abilities. The total number of genes encoding for cytochrome P450 monooxygenases and metabolic processes were high in soft and white rot fungi. We have tentatively calculated the overall lignocellulolytic abilities among the selected wood-rotting fungi which suggests that white rot fungi possess higher lignin and soft rot fungi potentially possess higher cellulolytic, hemicellulolytic and pectinolytic abilities. This approach can be applied industrially to efficiently find lignocellulolytic and aromatic compound-degrading fungi based on their genomic abilities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".