Bacterial genome hotspots no longer rest on a few summary statistics: neural posterior estimation separates recombination from mutation rates and assigns hotspot probabilities
Recombination and mutation rates vary along bacterial genomes, the two signals overlap, and clonal relationships confound inference, while earlier methods often assumed a constant mutation rate or relied on a small set of summary statistics. This work trains a simulator-based neural posterior estimator conditioned on a fixed clonal genealogy to infer local recombination and mutation rates for each gene segment, then applies a circular posterior-propagated Difference-of-Gaussians detector that carries posterior uncertainty into hotspot calls. On simulated data the inferred rates track the true values and credible intervals narrow as gene length grows; candidate hotspots are reported for three real datasets in the Bacillus cereus group, Staphylococcus aureus and Escherichia coli.