Genomic and evolutionary factors influencing the prediction accuracy of optimal growth temperature in prokaryotes
Bibliographic record
Abstract
Bacteria and archaea have evolved diverse genomic adaptations to thrive across various temperatures. These adaptations include genome sequence optimizations, such as increased GC content in rRNA and tRNA, shifts in codon and amino acids usage, and the acquisition of functional genes conferring adaptation for specific temperatures. Since the experimental determination of optimal growth temperatures (OGT) is only possible for cultured species, predicting OGT from genomic information has become increasingly important given the exponential increase in genomic data. Although previous studies developed prediction models integrating multiple features based on genome composition using machine learning, the accuracy was variable depending on the target species, with models performing well for thermophiles but less accurately for psychrophiles. In this study, we curated the OGT and genomic data of 2,869 bacterial species to develop a novel prediction model incorporating features reflecting genomic adaptation toward lower temperatures. We found that species with rapid OGT shifts from their ancestors, including psychrophiles, showed less accuracy in genome composition-based models. Incorporating the gene presence/absence information associated with the rapid changes in OGT improved the prediction accuracy for psychrophiles. We also observed that OGT in archaea is phylogenetically more conserved than in bacteria, which may lead to the long-term optimization of the genome composition and explain high predictability of OGT in archaea. These findings highlight the importance of integrating long- and short-term evolutionary adaptations for phenotype prediction models.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".