Jourdan F, Cottret L, Huc L, Wildridge D, Scheltema R, Hillenweck A, Barrett MP, Zalko D, Watson DG, Debrauwer L.
Metabolomics. 2010 Jun;6(2):312-321.
Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC–MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2874485/?tool=pubmedUMR1089, Xénobiotiques INRA-ENVT, 180 chemin de Tournefeuille, BP 93173, 31000 Toulouse Cedex 3, France
Division of Infection and Immunity and Wellcome Trust Centre for Molecular Parasitology, Glasgow Biomedical Research Centre, University of Glasgow,
Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands
Strathclyde Institute for Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
United States
