ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity

doi:10.1186/1471-2105-12-346

	ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity
	Zhang,Shenghui 1; Wang,Yaojun 1; Bu,Dongbo 1; Zhang,Hong 2; Sun,Shiwei 1
	2011-08-17
发表期刊	BMC Bioinformatics
ISSN	1471-2105
卷号	12 期号:1
摘要	AbstractBackgroundThe analysis of mass spectra suggests that the existence of derivative peaks is strongly dependent on the intensity of the primary peaks. Peak selection from tandem mass spectrum is used to filter out noise and contaminant peaks. It is widely accepted that a valid primary peak tends to have high intensity and is accompanied by derivative peaks, including isotopic peaks, neutral loss peaks, and complementary peaks. Existing models for peak selection ignore the dependence between the existence of the derivative peaks and the intensity of the primary peaks. Simple models for peak selection assume that these two attributes are independent; however, this assumption is contrary to real data and prone to error.ResultsIn this paper, we present a statistical model to quantitatively measure the dependence of the derivative peak's existence on the primary peak's intensity. Here, we propose a statistical model, named ProbPS, to capture the dependence in a quantitative manner and describe a statistical model for peak selection. Our results show that the quantitative understanding can successfully guide the peak selection process. By comparing ProbPS with AuDeNS we demonstrate the advantages of our method in both filtering out noise peaks and in improving de novo identification. In addition, we present a tag identification approach based on our peak selection method. Our results, using a test data set, suggest that our tag identification method (876 correct tags in 1000 spectra) outperforms PepNovoTag (790 correct tags in 1000 spectra).ConclusionsWe have shown that ProbPS improves the accuracy of peak selection which further enhances the performance of de novo sequencing and tag identification. Thus, our model saves valuable computation time and improving the accuracy of the results.
DOI	10.1186/1471-2105-12-346
语种	英语
WOS记录号	BMC:10.1186/1471-2105-12-346
出版者	BioMed Central
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/3986
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Sun,Shiwei
作者单位	1.Chinese Academy of Sciences; Institute of Computing Technology 2.Zhejiang Gongshang University
推荐引用方式 GB/T 7714	Zhang,Shenghui,Wang,Yaojun,Bu,Dongbo,et al. ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity[J]. BMC Bioinformatics,2011,12(1).
APA	Zhang,Shenghui,Wang,Yaojun,Bu,Dongbo,Zhang,Hong,&Sun,Shiwei.(2011).ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity.BMC Bioinformatics,12(1).
MLA	Zhang,Shenghui,et al."ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity".BMC Bioinformatics 12.1(2011).