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Nat Methods. 2017 Oct 23. doi: 10.1038/nmeth.4470. [Epub ahead of print]

Deciphering lipid structures based on platform-independent decision rules.

Author information

1
Institute of Computational Biotechnology, Graz University of Technology, Graz, Austria.
2
Center for Medical Research, Medical University of Graz, Graz, Austria.
3
Omics Center Graz, BioTechMed-Graz, Graz, Austria.
4
Department of Molecular Biosciences, University of Graz, Graz, Austria.
5
Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
6
Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
7
Singapore Lipidomics Incubator, National University of Singapore, Singapore.
8
Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
9
School of Medicine, University of California San Diego, La Jolla, California, USA.
10
The Babraham Institute, Babraham Research Campus, Cambridge, UK.
11
Department of Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria.

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.

PMID:
29058722
DOI:
10.1038/nmeth.4470
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