CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2011 vol.8 Issue No.02 - March/April
Issue No.02 - March/April (2011 vol.8)
Zengyou He , Hong Kong University of Science and Technology, Hong Kong
Can Yang , Hong Kong University of Science and Technology, Hong Kong
Weichuan Yu , Hong Kong University of Science and Technology, Hong Kong
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.54
Protein identification is a key and essential step in mass spectrometry (MS) based proteome research. To date, there are many protein identification strategies that employ either MS data or MS/MS data for database searching. While MS-based methods provide wider coverage than MS/MS-based methods, their identification accuracy is lower since MS data have less information than MS/MS data. Thus, it is desired to design more sophisticated algorithms that achieve higher identification accuracy using MS data. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from MS data for many years. In this paper, we extend this technology to protein mixture identification. First, we formulate the problem of protein mixture identification as a Partial Set Covering (PSC) problem. Then, we present several algorithms that can solve the PSC problem efficiently. Finally, we extend the partial set covering model to both MS/MS data and the combination of MS data and MS/MS data. The experimental results on simulated data and real data demonstrate the advantages of our method: 1) it outperforms previous MS-based approaches significantly; 2) it is useful in the MS/MS-based protein inference; and 3) it combines MS data and MS/MS data in a unified model such that the identification performance is further improved.
Protein identification, proteomics, peptide mass fingerprinting, mass spectrometry, set covering, linear programming, optimization.
Zengyou He, Can Yang, Weichuan Yu, "A Partial Set Covering Model for Protein Mixture Identification Using Mass Spectrometry Data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 2, pp. 368-380, March/April 2011, doi:10.1109/TCBB.2009.54