Rapid identification of polymorphic sequences in non‐model fungal species: the <scp>PHYLORPH</scp> method tested in <i>Armillaria</i> species
Bibliographic record
Abstract
Summary Development of molecular markers for phylogenetic, population genetics and phylogeographic studies remains arduous in non‐model species with low or no genomic resources. Sequencing the whole or a large part of the genome of the target species using next‐generation sequencing technologies is considered a promising method, although it still needs a large investment in bioinformatics. To quickly find polymorphic markers in fungal species, we tested an alternative method, named PHYLORPH. This method allows users to quickly target polymorphic regions of single copy genes in fungi using public databases. We applied this method to Armillaria species, which are important fungal pathogens and saprophytes playing a central role in the dynamics of forest ecosystems worldwide. We isolated 32 single copy genes with numerous single nucleotide polymorphism (SNP) sites. A genetic analysis of two French populations validated the polymorphism of 80 among 92 SNPs tested, and seven of these sequences exactly reconstructed the known phylogenetic tree of four tested Armillaria species. These results confirmed that the PHYLORPH method is efficient to identify various markers at both the intra‐ and interspecific levels for fungal species with no or few previous genetic markers.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".