Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

biological interpretation
data analysis
metabolomics
molecular networking
network analysis
review
  • Focus: This review highlights the use of network-based strategies as essential tools for analyzing and interpreting complex metabolomics data, especially from untargeted Mass Spectrometry (MS).
  • Molecular Networking (MN): Used to organize MS/MS spectral data based on chemical structural similarity, thereby facilitating the identification of unknown compounds in chemical families.
  • Metabolic Networks: Constructed using statistical correlation between metabolite abundance levels to infer functional and biological relationships, enabling mapping onto known biochemical pathways for biological contextualization.
Published

23 January 2026

PubMed: 32380880 DOI: 10.1080/14789450.2020.1766975 Overview generated by: Gemini 2.5 Flash, 28/11/2025

Key Focus: Network-Based Approaches for Metabolomics Data

This review article focuses on network-based strategies as essential computational tools for the analysis and interpretation of complex metabolomics data. It highlights how these methods move beyond simple compound identification and quantification to provide crucial insights into metabolic pathways and biological context.

The Challenge and the Solution

  • Challenge: Metabolomics datasets, especially those generated by untargeted Mass Spectrometry (MS), are vast and complex, containing numerous features (ions) that are often challenging to identify, map to biological functions, and integrate into a cohesive biological understanding.
  • Solution: Network analysis offers a powerful way to organize these complex datasets into visual and interpretable structures, revealing relationships between metabolites based on either chemical similarity (Molecular Networking) or biological correlation (Metabolic Network Reconstruction).

Network Strategies and Methods

The review divides network strategies into two main categories:

1. Molecular Networking (MN)

  • Goal: To organize MS/MS spectra of unknown compounds based on chemical structural similarity.
  • Mechanism: MN algorithms cluster compounds whose fragmentation spectra suggest they are structurally related (e.g., belong to the same chemical family or pathway), thereby facilitating the identification of unknown compounds in a large family once a single member is characterized.
  • Advanced Tools: The use of complementary tools like MolNetEnhancer is highlighted, which combines MN with chemical classification tools to provide structural family enrichment and better chemical context.

2. Metabolic Network Reconstruction

  • Goal: To infer functional and biological relationships between metabolites.
  • Correlation Networks: These networks use statistical correlation between metabolite abundance levels across different samples (or time points) to infer shared regulation or sequential steps in a metabolic pathway.
  • Biological Mapping: The ultimate aim is to map the identified metabolites and their relationships onto established biochemical pathways (e.g., KEGG, Reactome) to gain biological meaning and contextualize changes observed in response to a perturbation or disease state.

Applications and Importance

Network-based strategies are crucial for several areas in metabolomics:

  • Unknown Metabolite Identification: MN is especially valuable in natural product research and untargeted metabolomics for annotating large numbers of unknown spectral features.
  • Data Reduction and Visualization: Networks simplify highly dimensional data into intuitive visual representations that highlight key regulatory hubs or pathways.
  • Integration with Other Omics: The network approach facilitates the integration of metabolomics data with genomics, transcriptomics, and proteomics by mapping different molecular layers onto shared biological pathways.

Conclusion

The review concludes that network-based approaches are evolving rapidly, moving from basic visualization tools to sophisticated platforms for chemical and biological interpretation. They are indispensable for handling the inherent complexity of the metabolome and translating raw data into meaningful biological insights.