Teknonaturalist: A Snakemake Pipeline for Assessing Fungal Diversity From Plant Genome Bycatch
Bibliographic record
Abstract
Relatively little is known of the host associations and compatibility of fungal plant pathogens and endophytes. Publicly available plant genomic DNA can be mined to detect incidental fungal DNA, but taxonomic assignment can be challenging due to short lengths and variable discriminative power among different genomic regions and taxa. Here, we introduce a computationally lightweight and accessible Snakemake pipeline for rapid detection and classification (identification and assignment to taxonomic rank) of pathogenic and endophytic fungi (and other fungi associated with plants) that targets the internal transcribed spacer (ITS) region, a fungal barcode standard. We include methods for maximising query sequence length, which gives higher support for ITS1 and ITS2 taxonomic classifications by extending to other fragments of the ITS region and providing taxon-specific local cut-off and confidence scores. We demonstrate our pipeline with a case study using public genomic sequence data for six diverse plant species, including four species within Betula, an ecologically and economically important broadleaved forest tree genus, a shrub and a grass. Our pipeline classified fungi within minutes to a few hours per host individual, with 204 different fungal genera identified at high confidence (≥ 70%). Our pipeline detected and classified pathogenic and endophytic genera known to associate with Betula, and many others with no prior record of association. Our pipeline, leveraging existing sequence data, has several potential applications, including detecting cryptic fungal pathogens and helping characterise the endophytic fungal microbiome, bioprospecting commercially useful fungal species, and determining the plant host range of 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".