Studies of the pangenome have been empowered by an exponentially increasing amount of strain-specific genome sequencing data. With this data deluge comes a need for new tools to contextualize, analyze, and interpret such a vast amount of information. Network reconstructions, genome-scale metabolic models (GEMs), and the corresponding computational analysis frameworks such as flux balance analysis (FBA) have been proven useful toward this end. Network reconstructions can be used to interpret genomic variation not just from a single strain but for an entire species. By applying these approaches at the pangenome scale, it becomes possible to systematically evaluate phenotypic properties for an entire species thus enabling the study of a panphenome directly from a pangenome. Applying insights gained from analysis of the panphenome has diverse implications with applications ranging from human health to metabolic engineering. The future of pangenomics will include panphenomic analyses, thus supplementing traditional pangenomic analyses and helping to address the Big-data-to-knowledge grand challenge of analyzing thousands of genomic sequences.
Copyright 2020, The Author(s).