Identification and Characterization of Small Noncoding RNAs in Genome Sequences of the Edible Fungus<i>Pleurotus ostreatus</i>
Bibliographic record
Abstract
Noncoding RNAs (ncRNAs) have been identified in many fungi. However, no genome-scale identification of ncRNAs has been inventoried for basidiomycetes. In this research, we detected 254 small noncoding RNAs (sncRNAs) in a genome assembly of an isolate (CCEF00389) ofPleurotus ostreatus, which is a widely cultivated edible basidiomycetous fungus worldwide. The identified sncRNAs include snRNAs, snoRNAs, tRNAs, and miRNAs. SnRNA U1 was not found in CCEF00389 genome assembly and some other basidiomycetous genomes by BLASTn. This implies that if snRNA U1 of basidiomycetes exists, it has a sequence that varies significantly from other organisms. By analyzing the distribution of sncRNA loci, we found that snRNAs and most tRNAs (88.6%) were located in pseudo-UTR regions, while miRNAs are commonly found in introns. To analyze the evolutionary conservation of the sncRNAs inP. ostreatus, we aligned all 254 sncRNAs to the genome assemblies of some other Agaricomycotina fungi. The results suggest that most sncRNAs (77.56%) were highly conserved inP. ostreatus, and 20% were conserved in Agaricomycotina fungi. These findings indicate that most sncRNAs ofP. ostreatuswere not conserved across Agaricomycotina fungi.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".