Best-first search guided multistage mass spectrometry-based glycan identification

doi:10.1093/bioinformatics/btz056

	Best-first search guided multistage mass spectrometry-based glycan identification
	Wang, Yaojun 1,2; Bu, Dongbo 1; Huang, Chuncui 3; Wang, Hui 1,4; Zhou, Jinyu 3,4; Dong, Junchuan 1,4; Pan, Weiyi 1,4; Zhang, Jingwei 1,4; Zhang, Qi 1,4; Li, Yan 3; Sun, Shiwei 1
	2019-09-01
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	35 期号:17 页码:2991-2997
摘要	Motivation: Glycan identification has long been hampered by complicated branching patterns and various isomeric structures of glycans. Multistage mass spectrometry (MSn) is a promising glycan identification technique as it generates multiple-level fragments of a glycan, which can be explored to deduce branching pattern of the glycan and further distinguish it from other candidates with identical mass. However, the automatic glycan identification still remains a challenge since it mainly relies on expertise to guide a MSn instrument to generate spectra. Results: Here, we proposed a novel method, named bestFSA, based on a best-first search algorithm to guide the process of spectrum producing in glycan identification using MSn. BestFSA is able to select the most appropriate peaks for next round of experiments and complete the identification using as few experimental rounds. Our analysis of seven representative glycans shows that bestFSA correctly distinguishes actual glycans efficiently and suggested bestFSA could be used in practical glycan identification. The combination of the MSn technology coupled with bestFSA should greatly facilitate the automatic identification of glycan branching patterns, with significantly improved identification sensitivity, and reduce time and cost of MSn experiments.
DOI	10.1093/bioinformatics/btz056
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFC0910405] ; National Natural Science Foundation of China[31671369] ; National Natural Science Foundation of China[31600650] ; National Natural Science Foundation of China[31770775] ; International Partnership Program of Chinese Academy of Sciences[153311KYSB20150012]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000487323400014
出版者	OXFORD UNIV PRESS
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4610
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Yan; Sun, Shiwei
作者单位	1.Chinese Acad Sci, Inst Comp & Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 2.Peking Univ, Guanghua Sch Management, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Biophys, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yaojun,Bu, Dongbo,Huang, Chuncui,et al. Best-first search guided multistage mass spectrometry-based glycan identification[J]. BIOINFORMATICS,2019,35(17):2991-2997.
APA	Wang, Yaojun.,Bu, Dongbo.,Huang, Chuncui.,Wang, Hui.,Zhou, Jinyu.,...&Sun, Shiwei.(2019).Best-first search guided multistage mass spectrometry-based glycan identification.BIOINFORMATICS,35(17),2991-2997.
MLA	Wang, Yaojun,et al."Best-first search guided multistage mass spectrometry-based glycan identification".BIOINFORMATICS 35.17(2019):2991-2997.