Improve accuracy and sensibility in glycan structure prediction by matching glycan isotope abundance

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DOIResolve DOI: http://doi.org/10.1016/j.aca.2012.07.009
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TypeArticle
Journal titleAnalytica Chimica Acta
Volume743
Pages8089
SubjectBioinformatics; Glycan; Isotope; Mass spectrometry
AbstractMass Spectrometry (MS) is a powerful technique for the determination of glycan structures and is capable of providing qualitative and quantitative information. Recent development in computational method offers an opportunity to use glycan structure databases and de novo algorithms for extracting valuable information from MS or MS/MS data. However, detecting low-intensity peaks that are buried in noisy data sets is still a challenge and an algorithm for accurate prediction and annotation of glycan structures from MS data is highly desirable. The present study describes a novel algorithm for glycan structure prediction by matching glycan isotope abundance (mGIA), which takes isotope masses, abundances, and spacing into account. We constructed a comprehensive database containing 808 glycan compositions and their corresponding isotope abundance. Unlike most previously reported methods, not only did we take into count the m/z values of the peaks but also their corresponding logarithmic Euclidean distance of the calculated and detected isotope vectors. Evaluation against a linear classifier, obtained by training mGIA algorithm with datasets of three different human tissue samples from Consortium for Functional Glycomics (CFG) in association with Support Vector Machine (SVM), was proposed to improve the accuracy of automatic glycan structure annotation. In addition, an effective data preprocessing procedure, including baseline subtraction, smoothing, peak centroiding and composition matching for extracting correct isotope profiles from MS data was incorporated. The algorithm was validated by analyzing the mouse kidney MS data from CFG, resulting in the identification of 6 more glycan compositions than the previous annotation and significant improvement of detection of weaker peaks compared with the algorithm previously reported.
Publication date
LanguageEnglish
AffiliationHuman Health Therapeutics; National Research Council Canada
Peer reviewedYes
IdentifierS0003267012010100
NPARC number21268611
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Record identifierde878a80-0383-480b-a7f8-14549d3e0637
Record created2013-10-28
Record modified2016-05-09
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