• William Duren, Terry Weymouth, Tim Hull, Gilbert S Omenn, Brian Athey, Charles Burant, and Alla Karnovsky.
• Department 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.
• Bioinformatics. 2014 Aug 1;30(15):2239-41.

MotivationIn 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 ImplementationMetDisease 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.orgContactakarnovs@med.umich.eduSupplementary InformationSupplementary Data are available at Bioinformatics online.© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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