GPA: An algorithm for LC/MS based glycan profile annotation

Minkun Wang, Guoqiang Yu, Yehia Mechref, Habtom W. Ressom

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Glycomics helps investigate the role glycosylation plays in complex diseases. Liquid chromatography (LC) coupled with mass spectrometry (MS) is routinely used to profile the glycans released from proteins in a biological sample. This enables us to compare observed glycans and their abundances among different biological samples to discover candidate biomarkers. One of the challenges in label-free LC/MS-based glycan profiling is the presence of various charge states and derived adduct ions. We propose a novel Glycan Profile Annotation (GPA) algorithm to automatically cluster and annotate these ions using a graphical model. Specifically, GPA aims to generate a list of unique neutral masses representing putative glycan composition derived from various charge states and multiple adducts. We demonstrate the performance of GPA in recognizing ions derived from the same glycan through analysis of LC/MS data from a serum biomarker discovery study. In addition, a simulation study is carried out to evaluate GPA's performance against existing tools in handling ambiguous cases.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Pages16-22
Number of pages7
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 - Shanghai, China
Duration: Dec 18 2013Dec 21 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013

Conference

Conference2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Country/TerritoryChina
CityShanghai
Period12/18/1312/21/13

Keywords

  • LC/MS
  • biomarker discovery
  • glycan profile annotation
  • graphical model

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