Automatic annotation and visualization tool for mass spectrometry based glycomics.
BACKGROUND
With the development of glycomics, a large number of glycan structures have been determined by using mass spectrometry (MS)-based techniques. However, most glycan MS data needs to be manually annotated which is time-consuming, unreliable and inaccurate.


METHODS
Herein we report a tool for automatically annotating and browsing N-glycan masses and isotopic distributions. We first constructed a training dataset using the Consortium for Functional Glycomics database, in conjunction with data preprocessing and filtering by composition matching. In addition, we improved a matching glycan isotope abundance algorithm through identifying potential overlap region and constructing an optimization model so that it can deconvolute the overlapped glycan isotopic clusters.


RESULTS
In the matching process, if the m/z difference of two detected ions was close to an integer from 1 to 5, the m/z range was considered as a potential overlapped region, from the lower m/z to m/z + 5. It was found that there were more than 20 potential overlap regions in each group of data from CHO sample and human testing sample. Because the training dataset was imbalanced, we combined the Supporting Vector Machines (SVMs) algorithm with different sampling techniques, including Synthetic Minority Over-sampling Technique (SMOTE), to classify all potential candidate compositions. The results demonstrated an average of 26.8% increase in annotation sensitivity through the SMOTE-SVMs algorithm. The source code can be obtained from https://sourceforge.net/projects/glycomaid/.


CONCLUSIONS
We have developed a new tool which facilities high-throughput glycomics research and assists mass spectrometrists in the interpretation and annotation of glycan samples. Copyright © 2016 John Wiley & Sons, Ltd.
Hubei Co-Innovation Center of Basic Education Information Technology Services, Hubei University of Education, 430205, Wuhan, China.
Guang
G
Xu
Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China.
Xin
X
Liu
Human Health Therapeutics, National Research Council Canada, Ottawa, Ontario, Canada, K1A 0R6.|Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada, K1H 8M5.
Qing Yan
QY
Liu
Human Health Therapeutics, National Research Council Canada, Ottawa, Ontario, Canada, K1A 0R6.
Jianjun
J
Li
Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China.
Yanhong
Y
Zhou