Mining Metabolic Diversities Across Genomic Neighborhoods in Plants.

There is increasing evidence that genes in plant genomes are not randomly distributed. Notable examples are the recently identified biosynthetic gene clusters (BGCs), which encode various specialized metabolic pathways. The evolution of BGCs in plants is highly debated, mainly due to the complex interplay of gene duplication, cryptic genomic translocation, and selective pressures that drive their formation and diversification. We previously adopted genomic neighborhood (GN) analysis to identify and interpret evolutionary events and trajectories for BGCs specifically evolved in Brassicaceae. Here, we present step-by-step methods for conducting GN association analysis to identify significantly clustered genes, utilizing high-quality genomes from grass species as working materials. This pipeline provides a framework for understanding the organization (assessed by content similarity) and evolution (evaluated by sequence similarity) of plant BGCs, offering insights into the mechanisms underlying the distribution of genes involved in specialized metabolism.