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Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

1
The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand
2
Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Ecosciences Precinct, Dutton Park, Dutton Park, QLD 4102, Australia
3
Land and Water, Commonwealth Scientific and Industrial Research Organization (CSIRO), Research and Innovation Park, Acton, ACT 2601, Australia
4
Trajan Scientific and Medical, Ringwood, VIC 3134, Australia
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Bio21 Institute, The University of Melbourne, Parkville, VIC 3010, Australia
6
Department of Biological Sciences, National University of Singapore, Singapore 117411, Singapore
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Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD 4072, Australia
8
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada
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Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
*
Author to whom correspondence should be addressed.
Metabolites 2019, 9(4), 76; https://doi.org/10.3390/metabo9040076
Received: 25 March 2019 / Revised: 15 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
(This article belongs to the Special Issue Agribio and Food Metabolomics)
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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. View Full-Text
Keywords: mathematical modeling; data analysis; data integration; experimental design; quantitative omics; databases; translational metabolomics; pathway analysis; metabolic networks mathematical modeling; data analysis; data integration; experimental design; quantitative omics; databases; translational metabolomics; pathway analysis; metabolic networks
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Pinu, F.R.; Beale, D.J.; Paten, A.M.; Kouremenos, K.; Swarup, S.; Schirra, H.J.; Wishart, D. Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites 2019, 9, 76.

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