Abstract
The study of the microbiome, the totality of all microbes inhabiting the host or an environmental niche, has experienced exponential growth over the past few years. The microbiome contributes functional genes and metabolites, and is an important factor for maintaining health. In this context, metabolomics is increasingly applied to complement sequencing-based approaches (marker genes or shotgun metagenomics) to enable resolution of microbiome-conferred functionalities associated with health. However, analyzing the resulting multi-omics data remains a significant challenge in current microbiome studies. In this review, we provide an overview of different computational approaches that have been used in recent years for integrative analysis of metabolome and microbiome data, ranging from statistical correlation analysis to metabolic network-based modeling approaches. Throughout the process, we strive to present a unified conceptual framework for multi-omics integration and interpretation, as well as point out potential future directions.
Highlights
The human gut microbiome is a complex biological system that performs several vital functions for the host, such as digestion and degradation of macromolecules, production of vitamins, and training of the host immune system
Downstream omics technologies that analyze the transcriptome, proteome and metabolome have shown that significant genes identified through metagenomics might not necessarily be expressed [8,9]
In comparison, analyzing multi-omics data is still its infancy, usually tools developed the past decade to enable metabolome andinmicrobiome profilingrequiring development and application of advanced statistical algorithms to leverage multiple heterogeneous
Summary
The human gut microbiome is a complex biological system that performs several vital functions for the host, such as digestion and degradation of macromolecules, production of vitamins, and training of the host immune system. Much work has been done to analyze individual omics data, with many powerful bioinformatics tools developed over the past decade to enable metabolome and microbiome profiling [12,13,14,15,16,17]. In comparison, analyzing multi-omics data is still its infancy, usually tools developed the past decade to enable metabolome andinmicrobiome profilingrequiring [12,13,14,15,16,17]. We focus on recent progress and application of advanced statistical algorithms to leverage multiple heterogeneous yet interconnected applications of different computational strategies for integration of metabolomics in the context of data matrices. Computational strategies for integration of metabolomics in the context of microbiome studies
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