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Data processing for mass spectrometry-based metabolomics
Turku Centre for Biotechnology, Turku, Finland.
VTT Technical Research Centre of Finland, Espoo, Finland.ORCID iD: 0000-0002-2856-9165
2007 (English)In: Journal of Chromatography A, ISSN 0021-9673, E-ISSN 1873-3778, Vol. 1158, no 1-2, p. 318-328Article, review/survey (Refereed) Published
Abstract [en]

Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results. Increasing interest in metabolomics thus led to resurgence of interest in related data processing. A wide variety of methods and software tools have been developed for metabolomics during recent years, and this trend is likely to continue. In this paper we overview the key steps of metabolomic data processing and focus on reviewing recent literature related to this topic, particularly on methods for handling data from liquid chromatography mass spectrometry (LC-MS) experiments.

Place, publisher, year, edition, pages
Elsevier, 2007. Vol. 1158, no 1-2, p. 318-328
Keywords [en]
Metabolomics, Lipidomics, Proteomics, Normalization, Alignment, Liquid chromatography, Mass spectrometry, Feature extraction, Peak detection, Deconvolution
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-70908DOI: 10.1016/j.chroma.2007.04.021ISI: 000248418500025PubMedID: 17466315Scopus ID: 2-s2.0-34347405493OAI: oai:DiVA.org:oru-70908DiVA, id: diva2:1345885
Available from: 2019-08-26 Created: 2019-08-26 Last updated: 2019-09-05Bibliographically approved

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Oresic, Matej

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