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Molecular & Cellular Proteomics 6:527-536, 2007.
© 2007 by The American Society for Biochemistry and Molecular Biology, Inc.

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Technology

EBP, a Program for Protein Identification Using Multiple Tandem Mass Spectrometry Datasets^*,S

Thomas S. Price, Margaret B. Lucitt, Weichen Wu, David J. Austin, Angel Pizarro, Anastasia K. Yocum, Ian A. Blair^,¶, Garret A. FitzGerald^,|| and Tilo Grosser^,**

From the Institute for Translational Medicine and Therapeutics and Center for Cancer Pharmacology, University of Pennsylvania, Philadelphia, Pennsylvania 19104

MS/MS combined with database search methods can identify the proteins present in complex mixtures. High throughput methods that infer probable peptide sequences from enzymatically digested protein samples create a challenge in how best to aggregate the evidence for candidate proteins. Typically the results of multiple technical and/or biological replicate experiments must be combined to maximize sensitivity. We present a statistical method for estimating probabilities of protein expression that integrates peptide sequence identifications from multiple search algorithms and replicate experimental runs. The method was applied to create a repository of 797 non-homologous zebrafish (Danio rerio) proteins, at an empirically validated false identification rate under 1%, as a resource for the development of targeted quantitative proteomics assays. We have implemented this statistical method as an analytic module that can be integrated with an existing suite of open-source proteomics software.

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