title: Searching molecular structure databases with tandem mass spectra using CSI:FingerID.
abstract: Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.
authors.affiliation: Chair for Bioinformatics, Friedrich Schiller University, 07743 Jena, Germany;
authors.forename: Kai
authors.initials: K
authors.lastname: Dührkop
authors.affiliation: Helsinki Institute for Information Technology, Department of Computer Science, Aalto University, 02150 Espoo, Finland.
authors.forename: Huibin
authors.initials: H
authors.lastname: Shen
authors.affiliation: Chair for Bioinformatics, Friedrich Schiller University, 07743 Jena, Germany;
authors.forename: Marvin
authors.initials: M
authors.lastname: Meusel
authors.affiliation: Helsinki Institute for Information Technology, Department of Computer Science, Aalto University, 02150 Espoo, Finland.
authors.forename: Juho
authors.initials: J
authors.lastname: Rousu
authors.affiliation: Chair for Bioinformatics, Friedrich Schiller University, 07743 Jena, Germany; sebastian.boecker@uni-jena.de.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Böcker
mesh_terms: D000818:Animals; D030562:Databases, Protein; D006801:Humans; D000069550:Machine Learning; D013058:Mass Spectrometry; D055432:Metabolomics
keywords: bioinformatics; machine learning; mass spectrometry; metabolomics; small compound identification