摘要: The ongoing battle between humans and pathogenic bacteria has fueled rapid microbial evolution. Although whole-genome sequencing (WGS) has transformed the ability to track genomic mutations, existing tools lack comprehensive solutions for analyzing mutational patterns and their functional consequences in pathogenic bacteria. Here, we present CliPME, an innovative platform that bridges this critical gap by combining mutation detection, mutation effect prediction, and regulatory network analysis, specifically designed for bacterial genomics. We develop qMut, a high-performance R package designed for large-scale mutation profiling. Coupled with three major functional modules called MutFinder, MutAnalyzer, and ExpMiner, CliPME integrates population-level mutation analysis, functional mutation predictions, and estimation of gene-gene expression relationships. Using Mycobacterium tuberculosis (Mtb) as a case study, we show the power of CliPME by identifying functionally significant mutations in the transcription factor Rv0324, and experimentally demonstrate the link of its genetic variation to potential adaptive phenotypes. This resource empowers researchers to decode evolutionary mechanisms in bacterial pathogens and may accelerate the translation of genomic insights into antimicrobial strategies. The web server of CliPME is freely accessible at https://www.clipme.top/.