<p>Multi-omic studies in human microbiome research hold great potential for advancing our understanding of host–microbiome interactions. However, despite the growing availability of multi-omic datasets, analysing such data remains a major conceptual, analytical and computational challenge. Introduction of new multi-omic integration methods to address these challenges further complicates researchers’ efforts to navigate this expanding field. In this Review, we outline the landscape of multi-omic integration methods in the context of human microbiome research. In contrast to previous reviews, we specifically emphasize the different biological questions addressed by various integration approaches, including questions related to interactions between different molecular layers, molecular shifts that occur in disease, subgrouping of patients based on molecular profiles, and identification of biological mechanisms that underlie such associations. Our aim is to provide a timely, convenient and comprehensive resource for the microbiome research community, allowing researchers to identify the multi-omic integration approach that is best suited to their data and objectives.</p>

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Navigating multi-omic integration methods for human microbiome research

  • Efrat Muller,
  • Tal Bamberger,
  • Elhanan Borenstein

摘要

Multi-omic studies in human microbiome research hold great potential for advancing our understanding of host–microbiome interactions. However, despite the growing availability of multi-omic datasets, analysing such data remains a major conceptual, analytical and computational challenge. Introduction of new multi-omic integration methods to address these challenges further complicates researchers’ efforts to navigate this expanding field. In this Review, we outline the landscape of multi-omic integration methods in the context of human microbiome research. In contrast to previous reviews, we specifically emphasize the different biological questions addressed by various integration approaches, including questions related to interactions between different molecular layers, molecular shifts that occur in disease, subgrouping of patients based on molecular profiles, and identification of biological mechanisms that underlie such associations. Our aim is to provide a timely, convenient and comprehensive resource for the microbiome research community, allowing researchers to identify the multi-omic integration approach that is best suited to their data and objectives.