Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Arpana Vaniya, Oliver Fiehn

Research output: Contribution to journalArticlepeer-review

84 Scopus citations


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 (MS<sup>n</sup>) 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.

Original languageEnglish (US)
Pages (from-to)52-61
Number of pages10
JournalTrAC - Trends in Analytical Chemistry
StatePublished - Jun 1 2015


  • Fragmentation tree
  • Ion tree
  • Mass spectral tree
  • Mass spectrometry
  • Metabolite identification
  • Metabolomics
  • MS<sup>n</sup>
  • Multi-stage analysis
  • Tandem mass spectrometry
  • Unknown compound

ASJC Scopus subject areas

  • Analytical Chemistry
  • Spectroscopy
  • Environmental Chemistry


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