Systems biology and multi-omics integration: viewpoints from the metabolomics research community
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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.
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Other non-machine-learning challenges affect multi-omics studies, including study design, multi-site sample collection and management, data sharing and governance, visualisation, ethical standards, reproducibility and translation.
“However, there are few other challenges related to multi-omics studies which are not ML related such as study design (Haas et al., 2017), multi-site sample collection & management (Pinu et al., 2019), multi-site data sharing and governance (Saulnier et al., 2019), visualisation (Mougin et al., 2018), ethical standards (Lévesque et al., 2018), and finally making the research reproducible (Conesa”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Pinu et al., 2019
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“A remedy to that could be to use multi-layer models that would bring together various types of genomic and other -omic data [32, 33].”
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- Authors
- Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D.
- Affiliations
- Plant & Food Research (NZ)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- The University of Melbourne (AU)
- National University of Singapore (SG)
- The University of Queensland (AU)
- University of Alberta (CA)
- Corresponding author
- Pinu, Farhana R.
- ORCID
- Venue
- Metabolites
- Publisher
- MDPI AG
- Published
- 2019-04-18
- Volume
- 9
- Issue
- 4
- Pages
- 76
- ISSN
- 2218-1989 (ISSN-L)
- PMC
- PMC6523452
- OpenAlex
- W2938826237
- Cited by
- 723
- References
- 176
- Citations by year
- 2026: 104 · 2025: 108 · 2024: 106 · 2023: 84 · 2022: 94 · 2021: 124 · 2020: 79 · 2019: 23 · 2018: 1
- FWCI
- 26.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Metabolites (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Griffith Research Online (submittedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- multi-omics integration, metabolomics, systems biology, software tools, databases, genomics, transcriptomics, proteomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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