Predicting genomic traits in ammonia-oxidizing archaea using phylogenetic signals
Bibliographic record
Abstract
Abstract Phylogenetic conservatism of microbial traits has paved the way for phylogeny-based predictions, allowing us to move from descriptive to predictive functional microbial ecology. Here, we applied phylogenetic eigenvector mapping, an approach not previously used for microorganisms, to predict key traits of ammonia-oxidizing archaea (AOA), which are important players in nitrogen cycling. Using 168 nearly complete AOA genomes and metagenome assembled genomes from public databases, we predicted the distribution of 18 ecologically relevant genes across an updated amoA gene phylogeny, including a novel variant of an ammonia transporter found in this study. Of the selected genes, 94% displayed a significant phylogenetic signal and gene presence was predicted with >88% accuracy, >88% sensitivity, and >80% specificity. The phylogenetic eigenvector approach performed equally well as ancestral state reconstruction of traits. We implemented the predictive models on an amoA sequencing dataset of AOA soil communities and show key ecological predictions, e.g., that AOA communities in nitrogen rich soils have capacity for ureolytic metabolism while those adapted to low pH soils have the high affinity ammonia transporter ( amt2 ). Predicting genomic traits can shed light on the potential functions that microbes perform across earth biomes, further contributing to a better mechanistic understanding of their community assembly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".