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Mol Cell Proteomics DOI:10.1074/mcp.M600222-MCP200

PEPPeR, a platform for experimental proteomic pattern recognition.

Publication TypeJournal Article
Year of Publication2006
AuthorsJaffe, JD, Mani, DR, Leptos, KC, Church, GM, Gillette, MA, Carr, SA
JournalMol Cell Proteomics
Date Published2006 Oct
KeywordsAlgorithms, Animals, Biomarkers, Calibration, Mice, Mice, Inbred C57BL, Models, Theoretical, Normal Distribution, Pattern Recognition, Automated, Peptides, Proteomics

Quantitative proteomics holds considerable promise for elucidation of basic biology and for clinical biomarker discovery. However, it has been difficult to fulfill this promise due to over-reliance on identification-based quantitative methods and problems associated with chromatographic separation reproducibility. Here we describe new algorithms termed "Landmark Matching" and "Peak Matching" that greatly reduce these problems. Landmark Matching performs time base-independent propagation of peptide identities onto accurate mass LC-MS features in a way that leverages historical data derived from disparate data acquisition strategies. Peak Matching builds upon Landmark Matching by recognizing identical molecular species across multiple LC-MS experiments in an identity-independent fashion by clustering. We have bundled these algorithms together with other algorithms, data acquisition strategies, and experimental designs to create a Platform for Experimental Proteomic Pattern Recognition (PEPPeR). These developments enable use of established statistical tools previously limited to microarray analysis for treatment of proteomics data. We demonstrate that the proposed platform can be calibrated across 2.5 orders of magnitude and can perform robust quantification of ratios in both simple and complex mixtures with good precision and error characteristics across multiple sample preparations. We also demonstrate de novo marker discovery based on statistical significance of unidentified accurate mass components that changed between two mixtures. These markers were subsequently identified by accurate mass-driven MS/MS acquisition and demonstrated to be contaminant proteins associated with known proteins whose concentrations were designed to change between the two mixtures. These results have provided a real world validation of the platform for marker discovery.


Alternate JournalMol. Cell Proteomics
PubMed ID16857664
PubMed Central IDPMC2649820
Grant ListR01 CA126219 / CA / NCI NIH HHS / United States