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ConceptMetab: exploring relationships among metabolite sets to identify links among biomedical concepts.
Cavalcante, Raymond G; Patil, Snehal; Weymouth, Terry E; Bendinskas, Kestutis G; Karnovsky, Alla; Sartor, Maureen A.
Afiliación
  • Cavalcante RG; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
  • Patil S; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
  • Weymouth TE; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
  • Bendinskas KG; Department of Chemistry, State University of New York at Oswego, Oswego, NY 13126, USA.
  • Karnovsky A; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
  • Sartor MA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Bioinformatics ; 32(10): 1536-43, 2016 05 15.
Article en En | MEDLINE | ID: mdl-26794319
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Metabolómica Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Metabolómica Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido