DeltAMT: A Statistical Algorithm for Fast Detection of Protein Modifications From LC-MS/MS Data

doi:10.1074/mcp.M110.000455

	DeltAMT: A Statistical Algorithm for Fast Detection of Protein Modifications From LC-MS/MS Data
	Fu, Yan 1,2; Xiu, Li-Yun 1,2; Jia, Wei 3; Ye, Ding 1,2; Sun, Rui-Xiang 1,2; Qian, Xiao-Hong 3; He, Si-Min 1,2
	2011-05-01
发表期刊	MOLECULAR & CELLULAR PROTEOMICS
ISSN	1535-9476
卷号	10 期号:5 页码:15
摘要	Identification of proteins and their modifications via liquid chromatography-tandem mass spectrometry is an important task for the field of proteomics. However, because of the complexity of tandem mass spectra, the majority of the spectra cannot be identified. The presence of unanticipated protein modifications is among the major reasons for the low spectral identification rate. The conventional database search approach to protein identification has inherent difficulties in comprehensive detection of protein modifications. In recent years, increasing efforts have been devoted to developing unrestrictive approaches to modification identification, but they often suffer from their lack of speed. This paper presents a statistical algorithm named DeltAMT (Delta Accurate Mass and Time) for fast detection of abundant protein modifications from tandem mass spectra with high-accuracy precursor masses. The algorithm is based on the fact that the modified and unmodified versions of a peptide are usually present simultaneously in a sample and their spectra are correlated with each other in precursor masses and retention times. By representing each pair of spectra as a delta mass and time vector, bivariate Gaussian mixture models are used to detect modification-related spectral pairs. Unlike previous approaches to unrestrictive modification identification that mainly rely upon the fragment information and the mass dimension in liquid chromatography-tandem mass spectrometry, the proposed algorithm makes the most of precursor information. Thus, it is highly efficient while being accurate and sensitive. On two published data sets, the algorithm effectively detected various modifications and other interesting events, yielding deep insights into the data. Based on these discoveries, the spectral identification rates were significantly increased and many modified peptides were identified. Molecular & Cellular Proteomics 10: 10.1074/mcp.M110.000455, 1-15, 2011.
DOI	10.1074/mcp.M110.000455
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[30900262] ; National Natural Science Foundation of China[20735005] ; CAS[KGGX1-YW-13] ; National High Technology Research and Development Program (863) of China[2007AA02Z315] ; National High Technology Research and Development Program (863) of China[2008AA02Z309] ; National High Technology Research and Development Program (863) of China[2006AA02A308] ; National Key Basic Research and Development Program (973) of China[2010CB912701]
WOS研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemical Research Methods
WOS记录号	WOS:000290216700001
出版者	AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
引用统计	被引频次：32[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/2XEOYT63/13067
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Fu, Yan
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Key Lab & Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Beijing Inst Radiat Med, Beijing Proteome Res Ctr, State Key Lab Prote, Beijing 102206, Peoples R China
推荐引用方式 GB/T 7714	Fu, Yan,Xiu, Li-Yun,Jia, Wei,et al. DeltAMT: A Statistical Algorithm for Fast Detection of Protein Modifications From LC-MS/MS Data[J]. MOLECULAR & CELLULAR PROTEOMICS,2011,10(5):15.
APA	Fu, Yan.,Xiu, Li-Yun.,Jia, Wei.,Ye, Ding.,Sun, Rui-Xiang.,...&He, Si-Min.(2011).DeltAMT: A Statistical Algorithm for Fast Detection of Protein Modifications From LC-MS/MS Data.MOLECULAR & CELLULAR PROTEOMICS,10(5),15.
MLA	Fu, Yan,et al."DeltAMT: A Statistical Algorithm for Fast Detection of Protein Modifications From LC-MS/MS Data".MOLECULAR & CELLULAR PROTEOMICS 10.5(2011):15.