Probiogenomics as a Computational Biotechnology Framework: Safety-Gated Genome Analytics for Candidate Probiotic Prioritization and Validation.

Whole-genome sequencing has transformed probiotic discovery into a strain-resolved computational problem, but genome data alone cannot establish probiotic efficacy, complete safety, or regulatory acceptability. This review frames probiogenomics as a safety-gated decision-support framework for candidate probiotic prioritization and validation. The framework integrates strain provenance, genome quality, strain authentication, taxonomic confidence, antimicrobial resistance screening, virulence and toxin assessment, plasmid, prophage and mobile-element analysis, undesirable metabolite screening, functional prediction, comparative genomics, intended-use context, and validation planning. We emphasize that genome-based safety screening should be interpreted as early-stage risk triage under specified tools, databases, thresholds, and genome quality. Candidates with acceptable safety evidence can then be prioritized through pathway-level functional trait mining, comparative and evolutionary interpretation, systems biology, multi-omics, artificial intelligence or machine learning-assisted prioritization, and targeted phenotypic validation. However, predicted genes and pathways should be treated as hypotheses until expression, biological activity, product accumulation, or matched phenotypes are demonstrated under relevant host, product-matrix, dose, exposure-route, and application conditions. The framework is modular rather than one-size-fits-all: Human probiotics, animal feed probiotics, aquaculture probiotics, plant-associated beneficial microbes, starter cultures, dietary supplements, postbiotic source strains, and live biotherapeutic products require different safety questions, validation endpoints, environmental-release considerations, manufacturing controls, and regulatory pathways. We conclude that probiogenomics is most useful when it preserves uncertainty, reports negative and ambiguous findings, uses versioned and reproducible workflows, and links genome-derived predictions to auditable decision rules, an application-dependent evidence continuum, and context-specific validation.