Systematic review of publicly available non-Dikarya fungal proteomes for understanding their plant biomass-degrading and bioremediation potentials
Bibliographic record
Abstract
In the last two decades, studies on plant biomass-degrading fungi have remarkably increased to understand and reveal the underlying molecular mechanisms responsible for their life cycle and wood-decaying abilities. Most of the plant biomass-degrading fungi reported till date belong to basidiomycota or ascomycota phyla. Thus, very few studies were conducted on fungi belonging to other divisions. Recent sequencing studies have revealed complete genomic sequences of various fungi. Our present study is focused on understanding the plant biomass-degrading potentials, by retrieving genome-wide annotations of 56 published fungi belonging to Glomeromycota, Mucoromycota, Zoopagomycota, Blastocladiomycota, Chytridiomycota, Neocallimastigomycota, Microsporidia and Cryptomycota from JGI-MycoCosm repository. We have compared and analyzed the proteomic annotations, especially CAZy, KOG, KEGG and SM clusters by separating the proteomic annotations into lignin-, cellulose-, hemicellulose-, pectin-degrading enzymes and also highlighted the KEGG, KOG molecular mechanisms responsible for the metabolism of carbohydrates (lignocellulolytic pathways of fungi), complex organic pollutants, xenobiotic compounds, biosynthesis of secondary metabolites. However, we strongly agree that studying genome-wide distributions of fungal CAZyme does not completely corresponds to its biomass-degrading ability. Thus, our present study can be used as preliminary materials for selecting ideal fungal candidate for the degradation and conversion of plant biomass components, especially carbohydrates to bioethanol and other commercially valuable products.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".