title: Simple data-reduction method for high-resolution LC-MS data in metabolomics.
abstract: BACKGROUND
  Metabolomics LC-MS experiments yield large numbers of peaks, few of which can be identified by database matching. Many of the remaining peaks correspond to derivatives of identified peaks (e.g., isotope peaks, adducts, fragments and multiply charged molecules). In this article, we present a data-reduction approach that automatically identifies these derivative peaks.
  
  
  RESULTS
  Using data-driven clustering based on chromatographic peak shape correlation and intensity patterns across biological replicates, derivative peaks can be reliably identified. Using a test data set obtained from Leishmania donovani extracts, we achieved a 60% reduction of the number of peaks. After quality control filtering, almost 80% of the peaks could putatively be identified by database matching.
  
  
  CONCLUSIONS
  Automated peak filtering substantially speeds up the data-interpretation process.
authors.affiliation: Groningen Bioinformatics Centre, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Kerklaan 30, 9751 NN Haren, The Netherlands.
authors.forename: Ra
authors.initials: R
authors.lastname: Scheltema
authors.forename: S
authors.initials: S
authors.lastname: Decuypere
authors.forename: Jc
authors.initials: J
authors.lastname: Dujardin
authors.forename: Dg
authors.initials: D
authors.lastname: Watson
authors.forename: Rc
authors.initials: R
authors.lastname: Jansen
authors.forename: R
authors.initials: R
authors.lastname: Breitling
mesh_terms: D000465:Algorithms; D002457:Cell Extracts / Q000737:chemistry; D002853:Chromatography, Liquid / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D007893:Leishmania donovani / Q000737:chemistry; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D011786:Quality Control
chemical_list: D002457:Cell Extracts