title: Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics.
abstract: Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.
authors.affiliation: University of California Davis, Department of Chemistry, One Shields Avenue, Davis, CA 95616, USA ; University of California Davis, West Coast Metabolomics Center, Genome Center, 451 Health Sciences Drive, Davis, CA 95616, USA.
authors.forename: Arpana
authors.initials: A
authors.lastname: Vaniya
authors.affiliation: University of California Davis, West Coast Metabolomics Center, Genome Center, 451 Health Sciences Drive, Davis, CA 95616, USA ; King Abdulaziz University, Biochemistry Department, Jeddah, Saudi Arabia.
authors.forename: Oliver
authors.initials: O
authors.lastname: Fiehn
keywords: Fragmentation tree; Ion tree; MSn; Mass spectral tree; Mass spectrometry; Metabolite identification; Metabolomics; Multi-stage analysis; Tandem mass spectrometry; Unknown compound