Linear Discriminant Analysis-Based Estimation of the False Discovery Rate for Phosphopeptide Identifications

@article{citeulike:2690173, abstract = {Abstract: The development of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has made it possible to characterize phosphopeptides in an increasingly large-scale and high-throughput fashion. However, extracting confident phosphopeptide identifications from the resulting large data sets in a similar high-throughput fashion remains difficult, as does rigorously estimating the false discovery rate (FDR) of a set of phosphopeptide identifications. This article describes a data analysis pipeline designed to address these issues. The first step is to reanalyze phosphopeptide identifications that contain ambiguous assignments for the incorporated phosphate(s) to determine the most likely arrangement of the phosphate(s). The next step is to employ an expectation maximization algorithm to estimate the joint distribution of the peptide scores. A linear discriminant analysis is then performed to determine how to optimally combine peptide scores (in this case, from SEQUEST) into a discriminant score that possesses the maximum discriminating power. Based on this discriminant score, the p- and q-values for each phosphopeptide identification are calculated, and the phosphopeptide identification FDR is then estimated. This data analysis approach was applied to data from a study of irradiated human skin fibroblasts to provide a robust estimate of FDR for phosphopeptides. The Phosphopeptide FDR Estimator software is freely available for download at http://ncrr.pnl.gov/software/.}, address = {Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, and University of Michigan, School of Medicine, Ann Arbor, Michigan 48109}, author = {Du, Xiuxia and Yang, Feng and Manes, Nathan P. and Stenoien, David L. and Monroe, Matthew E. and Adkins, Joshua N. and States, David J. and Purvine, Samuel O. and Camp, Ii and Smith, Richard D. }, citeulike-article-id = {2690173}, doi = {10.1021/pr070510t}, journal = {J. Proteome Res.}, keywords = {peptide-identification-statistics, phosphopeptide, phosphorylation, proteomics, tandem-mass-spectrometry}, month = {April}, posted-at = {2008-04-25 15:56:45}, priority = {2}, title = {Linear Discriminant Analysis-Based Estimation of the False Discovery Rate for Phosphopeptide Identifications}, url = {http://dx.doi.org/10.1021/pr070510t}, year = {2008} }

TY - JOUR ID - citeulike:2690173 L3 - citeulike-article-id:2690173 TI - Linear Discriminant Analysis-Based Estimation of the False Discovery Rate for Phosphopeptide Identifications JF - J. Proteome Res. AD - Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, and University of Michigan, School of Medicine, Ann Arbor, Michigan 48109 N2 - Abstract: The development of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has made it possible to characterize phosphopeptides in an increasingly large-scale and high-throughput fashion. However, extracting confident phosphopeptide identifications from the resulting large data sets in a similar high-throughput fashion remains difficult, as does rigorously estimating the false discovery rate (FDR) of a set of phosphopeptide identifications. This article describes a data analysis pipeline designed to address these issues. The first step is to reanalyze phosphopeptide identifications that contain ambiguous assignments for the incorporated phosphate(s) to determine the most likely arrangement of the phosphate(s). The next step is to employ an expectation maximization algorithm to estimate the joint distribution of the peptide scores. A linear discriminant analysis is then performed to determine how to optimally combine peptide scores (in this case, from SEQUEST) into a discriminant score that possesses the maximum discriminating power. Based on this discriminant score, the p- and q-values for each phosphopeptide identification are calculated, and the phosphopeptide identification FDR is then estimated. This data analysis approach was applied to data from a study of irradiated human skin fibroblasts to provide a robust estimate of FDR for phosphopeptides. The Phosphopeptide FDR Estimator software is freely available for download at http://ncrr.pnl.gov/software/. KW - peptide-identification-statistics KW - phosphopeptide KW - phosphorylation KW - proteomics KW - tandem-mass-spectrometry AU - Du, Xiuxia AU - Yang, Feng AU - Manes, Nathan AU - Stenoien, David AU - Monroe, Matthew AU - Adkins, Joshua AU - States, David AU - Purvine, Samuel AU - Camp, Ii AU - Smith, Richard PY - 2008/04/19/ UR - http://dx.doi.org/10.1021/pr070510t ER -