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Bioinformatics Advance Access published online on December 10, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn636
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Glycan Family Analysis for Deducing N-Glycan Topology from Single MS

David Goldberg 1, Marshall Bern 1, Simon J. North 2, Stuart M. Haslam 2 and Anne Dell 2

1Palo Alto Research Center, 3333 Coyote Hill Rd, Palo Alto CA. 94304
2Division of Molecular Biosciences, Faculty of Natural Sciences, South Kensington Campus, Biochemistry Building,Imperial College, London SW7 2AZ, UK

*To whom correspondence should be addressed. Dr. David Goldberg, E-mail: goldberg{at}parc.com


  Abstract

Motivation: In the past few years, mass spectrometry (MS) has emerged as the premier tool for identi.cation and quanti.cation of biological molecules such as peptides and glycans. There are two basic strategies: single-MS, which uses a single round of mass analysis, and MS/MS (or higher-order MSn), which adds one or more additional rounds of mass analysis, interspersed with fragmentation steps. Single-MS offers higher throughput, broader mass coverage, and more direct quantitation, but generally much weaker identification. Single-MS, however, does work fairly well for the case of N-glycan identification, which are more constrained than other biological polymers. We previously demonstrated single-MS identification of N-glycans to the level of "cartoons" (monosaccharide composition and topology) by a system that incorporates an expert's detailed knowledge of the biological sample. In this paper, we explore the possibility of ab initio single-MS N-glycan identification, with the goal of extending single-MS, or primarily-single-MS, identification to non-expert users, novel conditions, and unstudied tissues.

Results: We propose and test three cartoon-assignment algorithms that make inferences informed by biological knowledge about glycan synthesis. To test the algorithms, we used 71 single-MS spectra from a variety of tissues and organisms, containing more than 2800 manually annotated peaks. The most successful of the algorithms computes the most richly connected subgraph within a "cartoon graph". This algorithm uniquely assigns the correct cartoon to more than half of the peaks in 41 out of the 71 spectra.

Contact: goldberg{at}parc.com

Associate Editor: Prof. John Quackenbush

Received on September 11, 2008; revised on November 9, 2008; accepted on December 6, 2008

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