This review explores current advancements in microbiome functional analysis enabled by next-generation sequencing technologies, which have transformed our understanding of microbial communities from mere taxonomic composition to their functional potential. We examine approaches that move beyond species identification to characterize microbial activities, interactions, and their roles in host health and disease. Genome-scale metabolic models allow for in-depth simulations of metabolic networks, enabling researchers to predict microbial metabolism, growth, and interspecies interactions in diverse environments. Additionally, computational methods for predicting metabolite profiles offer indirect insights into microbial metabolic outputs, which is crucial for identifying biomarkers and potential therapeutic targets. Functional pathway analysis tools further reveal microbial contributions to metabolic pathways, highlighting alterations in response to environmental changes and disease states. Together, these methods offer a powerful framework for understanding the complex metabolic interactions within microbial communities and their impact on host physiology. While significant progress has been made, challenges remain in the accuracy of predictive models and the completeness of reference databases, which limit the applicability of these methods in under-characterized ecosystems. The integration of these computational tools with multi-omic data holds promise for personalized approaches in precision medicine, allowing for targeted interventions that modulate the microbiome to improve health outcomes. This review highlights recent advances in microbiome functional analysis, providing a roadmap for future research and translational applications in human health and environmental microbiology.
With the advent of whole-genome sequencing, opportunities to investigate the population structure, transmission patterns, antimicrobial resistance profiles, and virulence determinants of Streptococcus pneumoniae at high resolution have been increasingly expanding. Consequently, a user-friendly bioinformatics tool is needed to automate the analysis of Streptococcus pneumoniae whole-genome sequencing data, summarize clinically relevant genomic features, and further guide treatment options. Here, we developed PneusPage, a web-based tool that integrates functions for species prediction, molecular typing, drug resistance determination, and data visualization of Streptococcus pneumoniae. To evaluate the performance of PneusPage, we analyzed 80 pneumococcal genomes with different serotypes from the Global Pneumococcal Sequencing Project and compared the results with those from another platform, PathogenWatch. We observed a high concordance between the two platforms in terms of serotypes (100% concordance rate), multilocus sequence typing (100% concordance rate), penicillin-binding protein typing (88.8% concordance rate), and the Global Pneumococcal Sequencing Clusters (98.8% concordance rate). In addition, PneusPage offers integrated analysis functions for the detection of virulence and mobile genetic elements that are not provided by previous platforms. By automating the analysis pipeline, PneusPage makes whole-genome sequencing data more accessible to non-specialist users, including microbiologists, epidemiologists, and clinicians, thereby enhancing the utility of whole-genome sequencing in both research and clinical settings. PneusPage is available at
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