One of the hallmarks of eukaryotic pathogens is the ability to genetically adapt to environmental change, causing for instance frequent drug resistance. Even though genome instability has been recognized as a key driver for microbial adaptation, most available computational tools focus just on one mutation type or analytical step. To overcome this limitation and better understand the role of genetic changes in enhancing microbial pathogenicity we established GIP, a novel, powerful bioinformatic pipeline for comparative genome analysis across microbial populations. GIP allows batch processing of whole genome sequencing datasets, including read alignment, normalization, quantification of chromosomes, genes and genomic bins, and detection of single nucleotide and structural variants. GIP produces a comprehensive summary report providing sample statistics together with graphical representations of genomic features and tabulated results. GIP further includes a tool-suite that enables downstream custom comparisons of samples subsets. GIP is broadly applicable to different eukaryotic systems that exploit genome instability for adaptation, including Leishmania, Plasmodium, Candida, and cancer.