Deciphering lipid structures based on platform-independent decision rules.
Jürgen HartlerAlexander TrieblAndreas ZieglMartin TrötzmüllerGerald N RechbergerOana A ZeleznikKathrin A ZierlerFederico TortaAmaury Cazenave-GassiotMarkus R WenkAlexander FaulandCraig E WheelockAaron M ArmandoOswald QuehenbergerQifeng ZhangMichael J O WakelamGuenter HaemmerleFriedrich SpenerHarald C KöfelerGerhard G ThallingerPublished in: Nature methods (2017)
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.
Keyphrases
- high throughput
- tandem mass spectrometry
- liquid chromatography
- ultra high performance liquid chromatography
- high resolution mass spectrometry
- fatty acid
- mass spectrometry
- gas chromatography
- high performance liquid chromatography
- high resolution
- electronic health record
- machine learning
- data analysis
- dna methylation
- deep learning
- genome wide
- gene expression
- solid phase extraction
- single molecule