De novo assembly of complete circular mitochondrial genomes from 2,695 fungal species
Bibliographic record
Abstract
Fungal mitochondrial genomes are critical for understanding phylogenetics, evolution, and ecology of the Kingdom Fungi, yet they remain underrepresented in public databases. To address this, we developed a workflow to recover mitochondrial genomes from 12,902 fungal short read sequencing data housed in the Sequence Read Archive (SRA) records, assembling complete circular genomes from 2,695 species. This effort expanded fungal mitochondrial genome diversity by nearly 2.3X particularly in understudied phyla such as Mucoromycota (11X increase) and Zoopagomycota (8X increase). The new dataset contains novel yet undescribed mitochondrial genomes at numerous taxonomic levels, including 15 classes, 64 orders, 178 families, and 544 genera. Taxonomic analysis revealed broad ecological representation among the top-assembled species, including human pathogens (e.g., Cryptococcus tetragattii), plant pathogens (e.g., Melampsora larici-populina), edible mushrooms (e.g., Suillus luteus), and industrial fungi. By leveraging the not yet fully exploited SRA sequencing data, this study fills critical gaps in fungal mitochondrial genomics, tripling the currently known mitochondrial genome diversity of the Kingdom Fungi, and provides an extensive resource for phylogenetic and evolutionary research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".