Issue 1, 2024

Discovering life's directed metabolic (sub)paths to interpret human biochemical markers using the DSMN tool

Abstract

Metabolomics data analysis for phenotype identification commonly reveals only a small set of biochemical markers, often containing overlapping metabolites for individual phenotypes. Differentiation between distinctive sample groups requires understanding the underlying causes of metabolic changes. However, combining biomarker data with knowledge of metabolic conversions from pathway databases is still a time-consuming process due to their scattered availability. Here, we integrate several resources through ontological linking into one unweighted, directed, labeled bipartite property graph database for human metabolic reactions: the Directed Small Moleicules Network (DSMN). This approach resolves several issues currently experienced in metabolic graph modeling and data visualization for metabolomics data, by generating (sub)networks of explainable biochemical relationships. Three datasets measuring human biomarkers for healthy aging were used to validate the results from shortest path calculations on the biochemical reactions captured in the DSMN. The DSMN is a fast solution to find and visualize biological pathways relevant to sparse metabolomics datasets. The generic nature of this approach opens up the possibility to integrate other omics data, such as proteomics and transcriptomics.

Graphical abstract: Discovering life's directed metabolic (sub)paths to interpret human biochemical markers using the DSMN tool

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Article information

Article type
Paper
Submitted
14 Apr 2023
Accepted
06 Oct 2023
First published
10 Oct 2023
This article is Open Access
Creative Commons BY license

Digital Discovery, 2024,3, 81-98

Discovering life's directed metabolic (sub)paths to interpret human biochemical markers using the DSMN tool

D. Slenter, M. Kutmon, C. T. Evelo and E. L. Willighagen, Digital Discovery, 2024, 3, 81 DOI: 10.1039/D3DD00069A

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