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Updated: May 1, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
Published on: November 10, 2023
MetDisease--connecting metabolites to diseases via literature
William Duren1, Terry Weymouth1, Tim Hull1
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
Motivation:
In recent years, metabolomics has emerged as an approach to perform large-scale characterization of small molecules in biological systems. Metabolomics posed a number of bioinformatics challenges associated in data analysis and interpretation. Genome-based metabolic reconstructions have established a powerful framework for connecting metabolites to genes through metabolic reactions and enzymes that catalyze them. Pathway databases and bioinformatics tools that use this framework have proven to be useful for annotating experimental metabolomics data. This framework can be used to infer connections between metabolites and diseases through annotated disease genes. However, only about half of experimentally detected metabolites can be mapped to canonical metabolic pathways. We present a new Cytoscape 3 plug-in, MetDisease, which uses an alternative approach to link metabolites to disease information. MetDisease uses Medical Subject Headings (MeSH) disease terms mapped to PubChem compounds through literature to annotate compound networks.
Availability And Implementation:
MetDisease can be downloaded from http://apps.cytoscape.org/apps/metdisease or installed via the Cytoscape app manager. Further information about MetDisease can be found at http://metdisease.ncibi.org
Contact:
akarnovs@med.umich.edu
Supplementary Information:
Supplementary Data are available at Bioinformatics online.
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