Integrative multi-omics and bioinformatics in the post-genomic era: From high-throughput sequencing to microbial systems biology

Next-generation sequencing (NGS) has revolutionized microbial genomics by enabling high-throughput, accurate, and cost-effective exploration of microbial genetic and functional diversity. In the post-genomic era, integrating NGS with multi-omics approaches—including metagenomics, metatranscriptomics, proteomics, and metabolomics—has significantly enhanced systems-level understanding of microbial physiology, ecological interactions, metabolic regulation, and host–microbe relationships. These advances have accelerated applications in antimicrobial resistance surveillance, clinical diagnostics, environmental bioremediation, industrial biotechnology, and sustainable agriculture. This review critically examines recent advances in major sequencing platforms, including Illumina, PacBio, and Oxford Nanopore, with an emphasis on their comparative strengths, limitations, error profiles, and suitability for diverse microbial applications. We also discuss modern bioinformatics workflows for sequence assembly, annotation, microbial community profiling, and integrative multi-omics analysis, highlighting emerging frameworks for integrating heterogeneous datasets. In addition, the review explores the growing role of artificial intelligence and machine learning in microbial systems biology, including predictive modeling, automated functional annotation, and microbiome-based diagnostics. However, this review has a few limitations. Given the rapidly changing landscape of bioinformatics, we focus primarily on mainstream software pipelines, so some niche or newly released tools may be overlooked. Additionally, the review leans heavily toward bacteria and viruses rather than complex eukaryotic microbes, and we do not delve into specific hardware requirements. Despite remarkable progress, substantial challenges remain in data integration, reproducibility, computational scalability, batch-effect correction, and experimental validation of in silico predictions. We conclude that the convergence of advanced sequencing technologies, reproducible bioinformatics pipelines, and AI-driven systems modeling will play a pivotal role in decoding microbial complexity and advancing precision microbiology, environmental sustainability, and next-generation biotechnological innovation.