Our new paper has been published in Journal of Microbiology.
This review article discusses recent advancements in microbiome functional analysis, emphasizing the integration of next-generation sequencing (NGS) data with computational approaches such as genome-scale metabolic models (GEMs), metabolite prediction, and pathway inference. It highlights how these methods extend microbiome research beyond taxonomic profiling to explore microbial metabolic interactions, host-microbiome relationships, and their implications for health and disease. GEMs enable simulations of metabolic networks, allowing researchers to predict microbial metabolism and interspecies interactions, while metabolite prediction models provide insights into microbial metabolic outputs crucial for biomarker identification. Functional pathway analysis tools further elucidate microbial contributions to metabolic processes, helping to understand environmental and pathological influences on microbial communities. Despite significant progress, challenges remain in improving model accuracy and reference database completeness, limiting their application in diverse ecosystems. The review underscores the potential of integrating these computational tools with multi-omic data to advance microbiome-based precision medicine and environmental research.
Sungwon Jung. "Advances in functional analysis of the microbiome: Integrating metabolic modeling, metabolite prediction, and pathway inference with NextGeneration Sequencing data" Journal of microbiology, 63(1):e.2411006, 2025.
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