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The exponential growth of genomic data, particularly for microbes, has made pangenomic approaches a gold standard for large-scale comparative genomics. By capturing the full genomic diversity of a species rather than relying on a single reference, pangenomics has transformed microbial genomics, revealing the adaptive potential of bacteria and the evolutionary dynamics underlying functional diversity. Among available tools, PPanGGOLiN distinguishes itself through its graph-based model coupled with statistical gene partitioning. Here we present PPanGGOLiN v2, which introduces substantial improvements across three dimensions: new analytical features that expand what users can investigate, a comprehensive software architecture redesign that improves maintainability and extensibility, and performance improvements that address the computational demands of ever-growing genomic datasets.
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Arnoux et al. (2026) studied this question.
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