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Biology Direct

, 2:26

First Online: 05 November 2007Received: 19 October 2007Accepted: 05 November 2007


BackgroundThe key to mass-spectrometry-based proteomics is peptide identification, which relies on software analysis of tandem mass spectra. Although each search engine has its strength, combining the strengths of various search engines is not yet realizable largely due to the lack of a unified statistical framework that is applicable to any method.

ResultsWe have developed a universal scheme for statistical calibration of peptide identifications. The protocol can be used for both de novo approaches as well as database search methods. We demonstrate the protocol using only the database search methods. Among seven methods -SEQUEST v27 rev12, ProbID v1.0, InsPecT v20060505, Mascot v2.1, X!Tandem v1.0, OMSSA v2.0 and RAId DbS – calibrated, except for X!Tandem and RAId DbS most methods require a rescaling according to the database size searched. We demonstrate that our calibration protocol indeed produces unified statistics both in terms of average number of false positives and in terms of the probability for a peptide hit to be a true positive. Although both the protocols for calibration and the statistics thus calibrated are universal, the calibration formulas obtained from one laboratory with data collected using either centroid or profile format may not be directly usable by the other laboratories. Thus each laboratory is encouraged to calibrate the search methods it intends to use. We also address the importance of using spectrum-specific statistics and possible improvement on the current calibration protocol. The spectra used for statistical E-value calibration are freely available upon request.

Open peer reviewReviewed by Dongxiao Zhu nominated by Arcady Mushegian, Alexey Nesvizhskii nominated by King Jordan and Vineet Bafna. For the full reviews, please go to the Reviewers- comments section.

Electronic supplementary materialThe online version of this article doi:10.1186-1745-6150-2-26 contains supplementary material, which is available to authorized users.

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Autor: Gelio Alves - Aleksey Y Ogurtsov - Wells W Wu - Guanghui Wang - Rong-Fong Shen - Yi-Kuo Yu


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