ConceptMetab: exploring relationships among metabolite sets to identify links among biomedical concepts

Abstract

Motivation: Capabilities in the field of metabolomics have grown tremendously in recent years. Many existing resources contain the chemical properties and classifications of commonly identified metabolites. However, the annotation of small molecules (both endogenous and synthetic) to meaningful biological pathways and concepts still lags behind the analytical capabilities and the chemistry-based annotations. Furthermore, no tools are available to visually explore relationships and networks among functionally related groups of metabolites (biomedical concepts). Such a tool would provide the ability to establish testable hypotheses regarding links among metabolic pathways, cellular processes, phenotypes and diseases.

Results: Here we present ConceptMetab, an interactive web-based tool for mapping and exploring the relationships among 16 069 biologically defined metabolite sets developed from Gene Ontology, KEGG and Medical Subject Headings, using both KEGG and PubChem compound identifiers, and based on statistical tests for association. We demonstrate the utility of ConceptMetab with multiple scenarios, showing it can be used to identify known and potentially novel relationships among metabolic pathways, cellular processes, phenotypes and diseases, and provides an intuitive interface for linking compounds to their molecular functions and higher level biological effects.

Availability and implementation: http://conceptmetab.med.umich.edu

Contacts: akarnovsky@umich.edu or sartorma@umich.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

A diagrammatic view of how small molecules are annotated to concepts in ConceptMetab. PubChem compounds are associated with MeSH Terms via Metab2MeSH. Metabolites with KEGG IDs are associated with KEGG Pathways via their XML representation. Enzymes and GO terms are mapped to KEGG compounds through Entrez genes and KEGG reactions. Finally, PubChem and KEGG small molecules are linked via a dictionary used in Metab2MeSH

Fig. 2.

Percentage enrichments between concept types. Numbers in each cell are the percentage of enrichment tests between the respective concept types which were significant (FDR < 0.05). Observe that KEGG-based concepts tend to be more enriched with other KEGG-based concepts, and similarly for PubChem-based concepts

Fig. 3.

Screenshot of overview heatmap for diseases associated with Unfolded Protein Response. Each row represents a MeSH disease and each column is a compound. The UPR is associated with four main groups of diseases, defined by the types of overlapping compounds. From here, users may click to proceed to an interactive heatmap view

Fig. 4.

Bipartite metabolic pathway – disease network identified by ConceptMetab and displayed in Cytoscape. Black diamonds represent pathways; grey squares are diseases. The ovals in the center represent groups of several KEGG pathways, e.g. carbohydrate metabolism includes amino sugar, nucleotide sugar, galactose, fructose and mannose metabolism

Fig. 5.

ConceptMetab complete network. Network nodes represent concepts. By clicking on an edge user can obtain the information about compounds that are in common between concepts