Abstract
The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.
Highlights
Systems biology is an interdisciplinary research field that requires the combined contribution of chemists, biologists, mathematicians, physicists, and engineers to untangle the biology of complex livingMetabolites 2019, 9, 76; doi:10.3390/metabo9040076 www.mdpi.com/journal/metabolitesMetabolites 2019, 9, 76 systems by integrating multiple types of quantitative molecular measurements with well-designed mathematical models [1,2]
While large-scale omics data are becoming more accessible, and multi-omics studies are becoming much more frequent—real multi-omics integration remains very challenging. This is because many of the specific analytical tools and experimental designs traditionally used for individual omics disciplines are not sufficiently well-suited to permit proper comparisons or intelligent integration across multiple omics disciplines
The preferred collection methods, storage techniques, required quantity and choice of biological samples used for genomics studies are often not suited for metabolomics, proteomics or transcriptomics
Summary
Systems biology is an interdisciplinary research field that requires the combined contribution of chemists, biologists, mathematicians, physicists, and engineers to untangle the biology of complex living. While large-scale omics data are becoming more accessible, and multi-omics studies are becoming much more frequent—real multi-omics integration remains very challenging This is because many of the specific analytical tools and experimental designs traditionally used for individual omics disciplines (e.g., genomics, transcriptomics, and proteomics) are not sufficiently well-suited to permit proper comparisons or intelligent integration across multiple omics disciplines. Carefully integrated multi-omics data must be ‘deconstructed’ into single data sets before being deposited into omics-specific databases in order to make it publicly available These issues underline the fact that high-quality multi-omics studies require: 1) proper experimental design, 2) thoughtful selection, preparation, and storage of appropriate biological samples, 3) careful collection of quantitative multi-omics data and associated meta-data, 4). 2019, 9,designs, x methods and analytical tools used in metabolomics can facilitate multi-omics studies and data integration
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