Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

Neha Garg, Clifford A. Kapono, Yan Wei Lim, Nobuhiro Koyama, Mark J.A. Vermeij, Douglas Conrad, Forest Rohwer, Pieter C. Dorrestein

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

While in nucleotide sequencing, the analysis of DNA from complex mixtures of organisms is common, this is not yet true for mass spectrometric data analysis ofcomplex mixtures. The comparative analyses of mass spectrometry data of microbial communities at the molecular level is difficult to perform, especially in the context of a host. The challenge does not lie in generating the mass spectrometry data, rather much of the difficulty falls in the realm of how to derive relevant information from this data. The informatics based techniques to visualize and organize datasets are well established for metagenome sequencing; however, due to the scarcity of informatics strategies in mass spectrometry, it is currently difficult to cross correlate two very different mass spectrometry data sets from microbial communities and their hosts. We highlight that molecular networking can be used as an organizational tool of tandem mass spectrometry data, automated database search for rapid identification of metabolites, and as a workflow to manage and compare mass spectrometry data from complex mixtures of organisms. To demonstrate this platform, we show data analysis from hard corals and a human lung associated with cystic fibrosis.

Original languageEnglish (US)
Pages (from-to)719-727
Number of pages9
JournalInternational Journal of Mass Spectrometry
Volume377
Issue number1
DOIs
StatePublished - 2015
Externally publishedYes

Keywords

  • Complex mixtures
  • Cytoscape
  • Database search
  • Mass spectrometry
  • Molecular networking
  • Spectral matching

ASJC Scopus subject areas

  • Instrumentation
  • Condensed Matter Physics
  • Spectroscopy
  • Physical and Theoretical Chemistry

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