Discovering the hidden function in fungal genomes
Bibliographic record
Abstract
New molecular technologies have helped unveil previously unexplored facets of the genome beyond the canonical proteome, including microproteins and short ORFs, products of alternative splicing, regulatory non-coding RNAs, as well as transposable elements, cis-regulatory DNA, and other highly repetitive regions of DNA. In this Review, we highlight what is known about this ‘hidden genome’ within the fungal kingdom. Using well-established model systems as a contextual framework, we describe key elements of this hidden genome in diverse fungal species, and explore how these factors perform critical functions in regulating fungal metabolism, stress tolerance, and pathogenesis. Finally, we discuss new technologies that may be adapted to further characterize the hidden genome in fungi. New molecular technologies have helped unveil facets of the genome beyond the canonical proteome, such as microproteins and short ORFs, products of alternative splicing, regulatory non-coding RNAs, transposable elements, and cis-regulatory DNA. In this Review, Gervais & Shapiro highlight what is known about this ‘hidden genome’ in fungi.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".