title: Knowledge discovery in metabolomics: an overview of MS data handling.
abstract: While metabolomics attempts to comprehensively analyse the small molecules characterising a biological system, MS has been promoted as the gold standard to study the wide chemical diversity and range of concentrations of the metabolome. On the other hand, extracting the relevant information from the overwhelming amount of data generated by modern analytical platforms has become an important issue for knowledge discovery in this research field. The appropriate treatment of such data is therefore of crucial importance in order, for the data, to provide valuable information. The aim of this review is to provide a broad overview of the methodologies developed to handle and process MS metabolomic data, compare the samples and highlight the relevant metabolites, starting from the raw data to the biomarker discovery. As data handling can be further separated into data processing, data pre-treatment and data analysis, recent advances in each of these steps are detailed separately.
authors.affiliation: School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland.
authors.forename: Julien
authors.initials: J
authors.lastname: Boccard
authors.forename: Jean-Luc
authors.initials: JL
authors.lastname: Veuthey
authors.forename: Serge
authors.initials: S
authors.lastname: Rudaz
mesh_terms: D015415:Biomarkers / Q000378:metabolism; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*
chemical_list: D015415:Biomarkers