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Automated Analysis of Large-Scale NMR Data Generates Metabolomic Signatures and Links Them to Candidate Metabolites.
Khalili, Bita; Tomasoni, Mattia; Mattei, Mirjam; Mallol Parera, Roger; Sonmez, Reyhan; Krefl, Daniel; Rueedi, Rico; Bergmann, Sven.
Afiliación
  • Khalili B; Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
  • Tomasoni M; Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland.
  • Mattei M; Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
  • Mallol Parera R; Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland.
  • Sonmez R; Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
  • Krefl D; Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland.
  • Rueedi R; Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
  • Bergmann S; Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland.
J Proteome Res ; 18(9): 3360-3368, 2019 09 06.
Article en En | MEDLINE | ID: mdl-31318216
Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we tested the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of covarying features derived from principal component analysis (PCA), the iterative signature algorithm (ISA), and averaged correlation profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. On the basis of the number and quality of the matches, we concluded that ISA and ACP can robustly identify ten and nine metabolites, respectively, half of which were shared, while PCA did not produce any signatures with robust matches.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Líquidos Corporales / Proteínas / Resonancia Magnética Nuclear Biomolecular / Metabolómica Límite: Humans Idioma: En Revista: J Proteome Res Asunto de la revista: BIOQUIMICA Año: 2019 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Estados Unidos
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Líquidos Corporales / Proteínas / Resonancia Magnética Nuclear Biomolecular / Metabolómica Límite: Humans Idioma: En Revista: J Proteome Res Asunto de la revista: BIOQUIMICA Año: 2019 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Estados Unidos