Get access

Improving the sensitivity of MASCOT search results validation by combining new features with Bayesian nonparametric model

Authors

  • Jie Ma,

    1. State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P. R. China
    Search for more papers by this author
    • These authors contributed equally to the paper and were regarded as joint first authors.

  • Jiyang Zhang,

    1. State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P. R. China
    2. School of Mechanical Engineering and Automatization, National University of Defense Technology, Changsha, P. R. China
    Search for more papers by this author
    • These authors contributed equally to the paper and were regarded as joint first authors.

  • Songfeng Wu,

    1. State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P. R. China
    Search for more papers by this author
  • Dong Li,

    1. State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P. R. China
    Search for more papers by this author
  • Yunping Zhu,

    1. State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P. R. China
    Search for more papers by this author
  • Fuchu He

    Corresponding author
    1. State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, P. R. China
    2. Institutes of Biomedical Sciences, Fudan University, Shanghai, P. R. China
    • Beijing Proteome Research Center, No. 33 Life Science Park Road, Changping District, Beijing 102206, P. R. China Fax: +86-10-80705155
    Search for more papers by this author

Abstract

The probability-based search engine MASCOT has been widely used to identify peptides and proteins in shotgun proteomic research. Most subsequent quality control methods filter out ambiguous assignments according to the ion score and thresholds provided by MASCOT. On the basis of target-decoy database search strategy, we evaluated the performance of several filter methods on MASCOT search results and demonstrated that using filter boundaries on two-dimensional feature spaces, the MASCOT ion score and its relative score can improve the sensitivity of the filter process. Furthermore, using a linear combination of several characteristics of the assigned peptides, including the MASCOT scores, 15 previously employed features, and some newly introduced features, we applied a Bayesian nonparametric model to MASCOT search results and validated more correctly identified peptides in control and complex data sets than those could be validated by empirical score thresholds.

Ancillary