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

BMC Bioinformatics

Research Article

Dongbo Bu¹  Shenghui Zhang¹  Yaojun Wang¹  Shiwei Sun¹  Hong Zhang² 

[1] Institute of Computing Technology, Chinese Academy of Sciences, 100190, Beijing, China;Zhejiang Gongshang University, 310018, Zhejiang, China;

关键词: Neutral Loss;    Isotopic Shift;    Tandem Mass Spectrum;    Primary Peak;    Noise Peak;   

DOI  :  10.1186/1471-2105-12-346

 received in 2011-04-18, accepted in 2011-08-17,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundThe 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.

【 授权许可】

Unknown   
© Zhang et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.