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Improvements to the Percolator Algorithm for Peptide Identification from Shotgun Proteomics Data Sets

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NEC Labs America, Princeton, New Jersey 08540, Computer Science Department, New York University, New York, New York 10003, Department of Genome Sciences, University of Washington, Seattle, Washington 98195, Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm University, Sweden, and Department of Computer Science and Engineering, University of Washington, Seattle, Washington 98195
* To whom correspondence should be addressed. E-mail: [email protected]
†NEC Research.
⊥New York University.
‡Department of Genome Sciences, University of Washington.
§Stockholm University.
∥Department of Computer Science and Engineering, University of Washington.
Cite this: J. Proteome Res. 2009, 8, 7, 3737–3745
Publication Date (Web):April 23, 2009
Copyright © 2009 American Chemical Society

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    Shotgun proteomics coupled with database search software allows the identification of a large number of peptides in a single experiment. However, some existing search algorithms, such as SEQUEST, use score functions that are designed primarily to identify the best peptide for a given spectrum. Consequently, when comparing identifications across spectra, the SEQUEST score function Xcorr fails to discriminate accurately between correct and incorrect peptide identifications. Several machine learning methods have been proposed to address the resulting classification task of distinguishing between correct and incorrect peptide-spectrum matches (PSMs). A recent example is Percolator, which uses semisupervised learning and a decoy database search strategy to learn to distinguish between correct and incorrect PSMs identified by a database search algorithm. The current work describes three improvements to Percolator. (1) Percolator’s heuristic optimization is replaced with a clear objective function, with intuitive reasons behind its choice. (2) Tractable nonlinear models are used instead of linear models, leading to improved accuracy over the original Percolator. (3) A method, Q-ranker, for directly optimizing the number of identified spectra at a specified q value is proposed, which achieves further gains.

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