Methodology article

A nonparametric model for quality control of database search results in shotgun proteomics

Jiyang Zhang^1,2 , Jianqi Li² , Xin Liu² , Hongwei Xie¹ , Yunping Zhu² and Fuchu He^1,2

¹College of Mechanical & Electronic Engineering and Automatization, National University of Defense Technology, Changsha, 410073, China

²State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China

author email corresponding author email

BMC Bioinformatics 2008, 9:29doi:10.1186/1471-2105-9-29

Published:	21 January 2008

Abstract

Background

Analysis of complex samples with tandem mass spectrometry (MS/MS) has become routine in proteomic research. However, validation of database search results creates a bottleneck in MS/MS data processing. Recently, methods based on a randomized database have become popular for quality control of database search results. However, a consequent problem is the ignorance of how to combine different database search scores to improve the sensitivity of randomized database methods.

Results

In this paper, a multivariate nonlinear discriminate function (DF) based on the multivariate nonparametric density estimation technique was used to filter out false-positive database search results with a predictable false positive rate (FPR). Application of this method to control datasets of different instruments (LCQ, LTQ, and LTQ/FT) yielded an estimated FPR close to the actual FPR. As expected, the method was more sensitive when more features were used. Furthermore, the new method was shown to be more sensitive than two commonly used methods on 3 complex sample datasets and 3 control datasets.

Conclusion

Using the nonparametric model, a more flexible DF can be obtained, resulting in improved sensitivity and good FPR estimation. This nonparametric statistical technique is a powerful tool for tackling the complexity and diversity of datasets in shotgun proteomics.