Bacteria adapt rapidly to their environments through the accumulation of mutations and the action of natural selection. These processes have major consequences for human health, agriculture, and industry, as harmful bacteria can quickly evolve resistance to treatments and colonize new environments. For this reason, identifying the genes that enable bacterial adaptation is of great importance. Current methods for identifying selection in bacterial genomes make overly simplistic assumptions about which mutations are harmful, beneficial, or neutral within a gene. The proposed work aims to develop improved methods for determining which mutations are truly neutral and which are most beneficial or deleterious. In addition, new datasets will be used to obtain more accurate estimates of the effects of different types of mutations on gene function. These advances will then be integrated into a new tool designed to more precisely estimate the strength and nature of selective pressures acting on individual genes. Overall, this project will improve the sensitivity of tests for selection and provide valuable new insights into bacterial evolution. The broader impacts of this project include delivering a new computational tool to the research community and that may facilitate advances in biotechnology, and providing training opportunities for students through research projects in microbiology, sequencing technologies, and high-performance computing. Selection is a core process shaping th