Data processing for mass spectrometry-based metabolomics

Mikko Katajamaa (Corresponding Author), Matej Orešič

Research output: Contribution to journalArticleScientificpeer-review

492 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)318-328
JournalJournal of Chromatography A
Issue number1-2
Publication statusPublished - 2007
MoE publication typeA1 Journal article-refereed


  • Metabolomics
  • Lipidomics
  • Proteomics
  • Normalization
  • Alignment
  • Liquid chromatography
  • Mass spectrometry
  • Feature extraction
  • Peak detection
  • Deconvolution


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