Models trained with noisy genomes extend bacterial phenotype prediction into deep time
Journal:
bioRxiv
Published Date:
Jul 2, 2026
Abstract
Predicting phenotype from genotype in extant organisms is increasingly tractable through the accumulation of genome sequences and the development of machine-learning algorithms. Here we show that machine learning can be applied to reconstructed ancestral gene content, extending these predictions into the past. We trained models on a diverse set of bacterial phenotypes and found that introducing noise into gene content profiles allows predictions to generalize over larger evolutionary distances. For phenotypes with signal spread across many genes - such as metabolic oxygen use, cell envelope architecture and optimal growth temperature - noise augmentation extends resolution back to the root of the bacterial domain, while for other phenotypes - including GC content and sporulation - the range remains more limited. We therefore conclude that the last bacterial common ancestor (LBCA) was likely an anaerobic, double-membraned, and moderately thermophilic bacterium (46-75{degrees}C). Moreover, this work provides a general approach for learning about the genomic basis of phenotypes and drawing inferences about their early evolution.
