Microbiome research has moved from cataloging community composition to asking what these communities do, but “function” is often used to describe fundamentally different levels of evidence. Functional claims may refer to functional capacity (what is encoded), functional realization (what molecular functions are actively engaged under specific conditions), or functional impact (the resulting consequences for hosts, microbial communities, or ecosystems). In this Perspective, we examine how functional annotations in microbiome research generate biological meaning and why current approaches support different kinds of inference. We propose a framework that distinguishes three levels of functional inference: capacity, realization, and impact. Homology-based, domain-centric, pathway-based, machine learning-driven, and multi-omics approaches each contribute differently to these levels, but none alone captures microbiome function in full. We argue that microbiome function should be interpreted as a hierarchy of inferences rather than a single property. Recognizing this distinction should improve how functional findings are interpreted, reported, and compared across microbiome studies. Accordingly, functional studies should explicitly state whether their conclusions concern capacity, realization, or impact, thereby clarifying the evidential basis of functional claims and improving their interpretation and comparison across microbiome studies.
Rajesh Kumar Bajiya, Rania Agabi, J. García et al.· Environmental Microbiome· 0 citations
Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science. In this Perspective, the authors discuss recent advances in multi-omics microbiome research, outlining key obstacles in data integration and metadata harmonization, and proposing a roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence advances.
T. Van Den Bossche, Eunice Lazau, Velma T. E. Aho et al.· Nature Communications· 0 citations
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