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Deciphering lipid structures based on platform-independent decision rules

  • Jürgen Hartler
  • , Alexander Triebl
  • , Andreas Ziegl
  • , Martin Trötzmüller
  • , Gerald N. Rechberger
  • , Oana A. Zeleznik
  • , Kathrin A. Zierler
  • , Federico Torta
  • , Amaury Cazenave-Gassiot
  • , Markus R. Wenk
  • , Alexander Fauland
  • , Craig E. Wheelock
  • , Aaron M. Armando
  • , Oswald Quehenberger
  • , Qifeng Zhang
  • , Michael J.O. Wakelam
  • , Guenter Haemmerle
  • , Friedrich Spener
  • , Harald C. Köfeler*
  • , Gerhard G. Thallinger
  • *Corresponding author for this work
  • Institute of Computational Biotechnology
  • Graz University of Technology
  • Center for Medical Research
  • Medical University of Graz
  • Omics Center Graz
  • BioTechMed-Graz
  • Department of Molecular Biosciences
  • University of Graz
  • Harvard University
  • Department of Medicine
  • National University of Singapore
  • Department of Medical Biochemistry and Biophysics
  • Karolinska Institutet
  • University of California at San Diego
  • Babraham Institute
  • Research Unit-Genetic Epidemiology

Research output: Contribution to journal › Article › peer-review

Abstract

We achieve automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry using decision rule sets embedded in Lipid Data Analyzer (LDA; http://genome.tugraz.at/lda2). Using various low- and high-resolution mass spectrometry instruments with several collision energies, we proved the method's platform independence. We propose that the software's reliability, flexibility, and ability to identify novel lipid molecular species may now render current state-of-the-art lipid libraries obsolete.

Original languageEnglish
Pages (from-to)1171-1174
Number of pages4
JournalNature Methods
Volume14
Issue number12
DOIs
Publication statusPublished - 1 Dec 2017
Externally publishedYes

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