The DOI matches its Crossref record; 128 of the 128 references it cites were found in a registry or an index.
Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation
Open accessAbstract
INTRODUCTION: Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system. AREAS COVERED: This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches. EXPERT OPINION: Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.
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“Many functional metabolomics studies do not measure functional properties of the metabolites themselves, but rather determine function using correlation and “guilt by association” with other known bioactive metabolites, transcription, or protein expression”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [18]
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Multi-omic approaches for host-microbiome data integration
Chetty, A., & Blekhman, R. (2024) · Gut Microbes · doi:10.1080/19490976.2023.2297860 · Cited by 199
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Metadata
- Authors
- Perez De Souza, L., Alseekh, S., Brotman, Y., & Fernie, A. R.
- Affiliations
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology (BG)
- Ben-Gurion University of the Negev (IL)
- Corresponding author
- Fernie, Alisdair R
- Venue
- Expert Review of Proteomics
- Publisher
- Informa UK Limited
- Published
- 2020-04-02
- Volume
- 17
- Issue
- 4
- Pages
- 243-255
- ISSN
- 1478-9450 (ISSN-L)
- 1744-8387
- OpenAlex
- W3021276280
- Cited by
- 145
- References
- 127
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 35 · 2023: 18 · 2022: 26 · 2021: 19 · 2020: 7
- FWCI
- 1.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Expert Review of Proteomics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- H2020 European Institute of Innovation and Technology (FPA No. 664620, No 739582, SGA-CSA No 664621)
- 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
- metabolomics, network analysis, molecular networking, association networks, metabolite annotation, biochemical reaction networks, metabolic networks, unknown compound identification, systems biology
- MeSH terms
- Information Systems, Systems Biology, Metabolomics
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