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: the Directed Small Molecules 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 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.
Discovering life’s directed metabolic (sub)paths to interpret biochemical markers using the DSMN
21 April 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.