Dataset Information

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

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.

SUBMITTER: Zhang J

PROVIDER: S-EPMC2267700 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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

Zhang Jiyang J Li Jianqi J Liu Xin X Xie Hongwei H Zhu Yunping Y He Fuchu F

BMC bioinformatics 20080121

<h4>Background</h4>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.<h4>Re ...[more]

PMID: 18205957

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Project description:The results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstances-a problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ?20,000 primary isoforms plus contaminants to a very large database that includes almost all nonredundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/ .

Dataset Information

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

Publications

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

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