Genome mining is the systematic computational interrogation of genome sequence data to identify, annotate, and functionally infer genetically encoded biological features from characteristic sequence patterns and genomic organization. Within bioinformatics and molecular genomics, the process integrates sequence similarity searches, protein-domain recognition, motif analysis, gene-neighborhood information, comparative genomics, and specialized predictive algorithms to locate genes and coordinated genomic regions associated with defined molecular functions ^[1][2][3]. A major established form of genome mining identifies biosynthetic gene clusters, in which physically associated genes collectively encode enzymes, regulatory proteins, transport functions, and accessory components of a biosynthetic pathway ^[1][2]. Computational pipelines may classify these regions according to conserved biosynthetic domains, pathway architecture, similarity to characterized loci, and predicted enzymatic functions ^[1][3]. Genome mining therefore encompasses the extraction of biologically interpretable gene, pathway, and genomic-network information directly from assembled or annotated genome sequences through computational analysis and functional prediction ^[1][2][3].