Predicting gene distribution in ammonia-oxidizing archaea using phylogenetic signals
Why this work is in the frame
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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 to predict the presence of genes indicating potential functions of ammonia-oxidizing archaea (AOA), which are important players in nitrogen cycling. Using 160 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. All selected genes displayed a significant phylogenetic signal and gene presence was predicted with an average of >88% accuracy, >85% sensitivity, and >80% specificity. The phylogenetic eigenvector approach performed equally well as ancestral state reconstruction of gene presence. We implemented the predictive models on an amoA sequencing dataset of AOA soil communities and showed key ecological predictions, e.g. that AOA communities in nitrogen-rich soils were predicted to have capacity for ureolytic metabolism while those adapted to low-pH soils were predicted to have the high-affinity ammonia transporter (amt2). Predicting gene presence can shed light on the potential functions that microorganisms perform in the environment, further contributing to a better mechanistic understanding of their community assembly.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it