Metabolites 2013, 3(4), 1036-1050; doi:10.3390/metabo3041036
Article

Computational Analyses of Spectral Trees from Electrospray Multi-Stage Mass Spectrometry to Aid Metabolite Identification

Received: 6 August 2013; in revised form: 7 October 2013 / Accepted: 16 October 2013 / Published: 31 October 2013
(This article belongs to the Special Issue Data Processing in Metabolomics)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Mass spectrometry coupled with chromatography has become the major technical platform in metabolomics. Aided by peak detection algorithms, the detected signals are characterized by mass-over-charge ratio (m/z) and retention time. Chemical identities often remain elusive for the majority of the signals. Multi-stage mass spectrometry based on electrospray ionization (ESI) allows collision-induced dissociation (CID) fragmentation of selected precursor ions. These fragment ions can assist in structural inference for metabolites of low molecular weight. Computational investigations of fragmentation spectra have increasingly received attention in metabolomics and various public databases house such data. We have developed an R package “iontree” that can capture, store and analyze MS2 and MS3 mass spectral data from high throughput metabolomics experiments. The package includes functions for ion tree construction, an algorithm (distMS2) for MS2 spectral comparison, and tools for building platform-independent ion tree (MS2/MS3) libraries. We have demonstrated the utilization of the package for the systematic analysis and annotation of fragmentation spectra collected in various metabolomics platforms, including direct infusion mass spectrometry, and liquid chromatography coupled with either low resolution or high resolution mass spectrometry. Assisted by the developed computational tools, we have demonstrated that spectral trees can provide informative evidence complementary to retention time and accurate mass to aid with annotating unknown peaks. These experimental spectral trees once subjected to a quality control process, can be used for querying public MS2 databases or de novo interpretation. The putatively annotated spectral trees can be readily incorporated into reference libraries for routine identification of metabolites.
Keywords: ESI fragmentation; peak annotation; metabolite identification; Lolium perenne
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MDPI and ACS Style

Cao, M.; Fraser, K.; Rasmussen, S. Computational Analyses of Spectral Trees from Electrospray Multi-Stage Mass Spectrometry to Aid Metabolite Identification. Metabolites 2013, 3, 1036-1050.

AMA Style

Cao M, Fraser K, Rasmussen S. Computational Analyses of Spectral Trees from Electrospray Multi-Stage Mass Spectrometry to Aid Metabolite Identification. Metabolites. 2013; 3(4):1036-1050.

Chicago/Turabian Style

Cao, Mingshu; Fraser, Karl; Rasmussen, Susanne. 2013. "Computational Analyses of Spectral Trees from Electrospray Multi-Stage Mass Spectrometry to Aid Metabolite Identification." Metabolites 3, no. 4: 1036-1050.

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