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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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Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
KEGG: new perspectives on genomes, pathways, diseases and drugs
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1092 · Cited by 8,865
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
Sparse inverse covariance estimation with the graphical lasso
Friedman, J., Hastie, T., & Tibshirani, R. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm045 · Cited by 6,355
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. A., & Siuzdak, G. E. (2006) · Analytical Chemistry · doi:10.1021/ac051437y · Cited by 5,296
The Large-Scale Organization of Metabolic Networks
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972
Wang, M., Carver, J. J., Phelan, V. V., Sanchez, L. M., Garg, N., Peng, Y., Nguyen, D. D., Watrous, J., Kapono, C. A., Luzzatto-Knaan, T., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3597 · Cited by 4,629
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903
Pluskal, T., Castillo, S., Villar‐Briones, A., & Orešič, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-395 · Cited by 3,852
Research works printed but not linked: 4
- Beleggia R, 2020, bioRxiv
- Nikoloski Z, 2015, Plant Physiol, 169, 1443
- Nothias LF, 2019, bioRxiv
- Aksenov A, 2020, bioRxiv
Cited by
Sorted by citations
Items 101–146 of 146
Wang, M., Wang, C., Furukawa, F., Lin, C.-C., Lin, T., & Tseng, Y. (2025) · Comparative Biochemistry and Physiology Part D Genomics and Proteomics · doi:10.1016/j.cbd.2025.101448 · Cited by 2
Kantari, S. A. K., Dharanikota, M., & Agraharam, A. (2021) · Springer eBooks · doi:10.1007/978-981-15-9371-0_11 · Cited by 2
Slenter, D. (2024) · Research Publications (Maastricht University) · doi:10.26481/dis.20240624ds · Cited by 2
Cell culture metabolomics and lipidomics
Alecu, I., Sosa‐Miranda, C. D., Sandhu, J. K., Bennett, S. A. L., & Čuperlović‐Culf, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85062-9.00012-x · Cited by 1
Metabolomics as a tool to study responses of plant growth-promoting rhizobacteria to stress
Malik, S., Saeed, A. A. I., & Hyder, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33594-5.00021-4 · Cited by 1
Singh, D. P., Maurya, S., Prabha, R., Satnami, L., & Rai, N. (2025) · Springer eBooks · doi:10.1007/978-981-95-2294-1_8 · Cited by 1
Fu, T., Francis, E. C., Kinkade, C. W., SPERLING, R. S., Qiu, Y., Kurland, I. J., Jao, J. K., & Shiau, S. (2025) · Metabolites · doi:10.3390/metabo15120765 · Cited by 1
Zheng, Q., Hong, N., Liu, L., Wang, C., Gan, R., Xu, D., & Wang, J. (2026) · Metabolites · doi:10.3390/metabo16020105 · Cited by 1
Kaya, C. (2026) · Plant Science · doi:10.1016/j.plantsci.2026.113025 · Cited by 1
Verespejova, A. I., Grendár, M., Kertys, M., Hunarova, N., Chien, L. S., Grofik, M., Škorvanová, M., Šofránko, J., Žideková, N., Kurča, E., et al. (2026) · Biomolecules · doi:10.3390/biom16060875 · Cited by 1
Liu, W., Huang, Y. H., Li, C., Song, G., Xiao, M., Shen, G., & Feng, J. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00021 · Cited by 1
Metabolic Effects of Healing Touch During Cervical Cancer Treatment: An Exploratory Analysis
van Wietmarschen, H., Área-Gómez, E., Picard, M. A., Goodheart, M. J., Sood, A. K., & Lutgendorf, S. K. (2025) · Integrative Cancer Therapies · doi:10.1177/15347354251367793 · Cited by 1
Disparity-filtered differential correlation network analysis: a case study on CRC metabolomics
Sabatini, S., & Gastaldelli, A. (2021) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2021-0030 · Cited by 1
Databases and Tools to Investigate Protein-Metabolite Interactions
de Souza, L. P., & Fernie, A. R. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2624-5_14 · Cited by 1
Self-citationEstimate Metabolite Taxonomy and Structure with a Fragment-Centered Database and Fragment NetworkPreprint
Zhao, H., Zhao, X., Yao, H., Feng, J., Zhang, S., & Zhang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03784
DEVELOPMENT OF TOOLS FOR ATOM-LEVEL INTERPRETATION OF STABLE ISOTOPE-RESOLVED METABOLOMICS DATASETS
Jin, H. (2021) · UKnowledge (University of Kentucky) · doi:10.13023/etd.2021.303
Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations
Ferrarini, M. G., Ziska, I., Andrade, R., Julien-Laferrière, A., Duchemin, L., Junior, R. M. C., Mary, A., Vinga, S., & Sagot, M. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.815476
Pancoro, A., Karima, E., Apriyanto, A., & Effendi, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-1535384/v1
Discovering life’s directed metabolic (sub)paths to interpret biochemical markers using the DSMNPreprint
Slenter, D., Kutmon, M., Evelo, C. T., & Willighagen, E. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2022-q1fv1-v2
Development of network‐guided transcriptomic risk score for disease prediction
Cao, X., Zhang, L., & Lee, K. (2024) · Stat · doi:10.1002/sta4.648
Molecular approaches to screen bioactive compounds from endophytic fungi
Arya, P., Yagnik, S. M., & Raval, V. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99314-2.00003-6
Metabolic network plasticity underpins Eucalyptus water use efficiency under drought
Müller, C., dos Anjos, L., Freire, F. B. S., Fernie, A. R., Daloso, D. M., Eller, C. B., & Merchant, A. (2025) · Environmental and Experimental Botany · doi:10.1016/j.envexpbot.2025.106127
Self-citationEnhancing Metabolomics Pathway Prediction with Sequential Graph Convolutional Network
Moozhippurath, B., & Jayapandian, N. (2025) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt64827.2025.10967932
Vásquez, J. O., Hurtado-Giraldo, J., & Bucheli, V. (2025) · Acta Biológica Colombiana · doi:10.15446/abc.v30n1.111588
Leibovich, N., & Čuperlović‐Culf, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.22.666141
Tian, Y., Giles, C., Biase, M. A. D., Cash, R. F. H., Cropley, V., & Zalesky, A. (2025) · medRxiv · doi:10.1101/2025.08.14.25333723
Gill, N. P., Balasubramanian, R., Bain, J. R., Muehlbauer, M. J., Lowe Jr., W. L., & Scholtens, D. M. (2022) · Figshare · doi:10.6084/m9.figshare.17913004
Gill, N. P., Balasubramanian, R., Bain, J. R., Muehlbauer, M. J., Lowe Jr., W. L., & Scholtens, D. M. (2022) · Figshare · doi:10.6084/m9.figshare.17913001
Subramanian, V., Dhandayuthapani, U. N., Kandasamy, S., Vaishnavi, S. J., Balasubramaniam, P., Shanmugam, M. K., Nagappan, S., Elangovan, S., Subramani, U. K., Palaniyappan, K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26720598
Studnicka, M. H., Bianchini, J. R., Felisberto, N. B., Silva, M. E. S. E., Pisano, J., Pereira, I. M. C., de Carvalho, H. H., Dalvi, V. C., Matsumura, E. E., Bogo, A., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-39107-0
Toubiana, D., Moon, P., Bassil, E., & Maruenda, H. (2026) · Scientific Reports · doi:10.1038/s41598-026-45899-y
Models and algorithms for investigating and exploiting the metabolism of microorganisms
Ziska, I. (2020) · theses.fr (ABES) · doi:10.70675/c33d1111z7e28z4809z88a7zea0485a6058f
Correland SW : Correlation Networks from LC–MS Data
Kosinova, A., & Grúz, J. (2026) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.6c00065
Leibovich, N., & Čuperlović‐Culf, M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0349617
Liu, R., Wang, D., Jin, G., Li, L., Wang, C., Wang, Y., Wang, K., Zhao, M., Zhang, M., & Zhang, M. (2026) · Plants · doi:10.3390/plants15131987
Tlhapi, D. B., Malebo, N. J., Manduna, I. T., Mawunu, M., Chukwuma, C. I., Chokwe, R. C., & Olofinsan, K. A. (2026) · Metabolites · doi:10.3390/metabo16080523
MetaboGraph: A Framework for Metabolomics and Lipidomics Annotation and Pathway Network Analysis
Daramola, O. E., Nwaiwu, J. I., Oluokun, O. O., Fowowe, M., & Mechref, Y. S. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.6c01446
Zhang, Y., Liu, P., Zhao, X., Chen, C., & Hou, Z. (2026) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2026.114654
Wei, P., Du, B., Zhang, X., Li, G., Li, F., Zhang, P., Dou, J., Zou, L., Yang, J., Wang, Y., et al. (2026) · Metabolites · doi:10.3390/metabo16090612
Metabolite correlation networks reveal complex phenotypes of adaptation-driving mutations
Samuel, S., Jasińska, W., Sternlicht, E., Nikelshparg, M. I., Kleiner, D., Pilosof, S., & Bershtein, S. (2026) · Nature Communications · doi:10.1038/s41467-026-77850-0
Dang, D. X., Li, D., Wang, H., Yang, J., Li, C. J., & Xu, S. (2026) · Cogent Food & Agriculture · doi:10.1080/23311932.2026.2722443
Data-Driven Mechanistic Modeling of Untargeted Metabolome Data
Mäkinen, M., & Chotteau, V. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5583-2_9
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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