Approximation algorithms for NP-hard problems
Improved performance of the greedy algorithm for partial cover
Information Processing Letters
Computing small partial coverings
Information Processing Letters
Approximation algorithms for partial covering problems
Journal of Algorithms
TANDEM: matching proteins with tandem mass spectra
Bioinformatics
A unified approach to approximating partial covering problems
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Bioinformatics
Peak bagging for peptide mass fingerprinting
Bioinformatics
A Bayesian approach to protein inference problem in shotgun proteomics
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
ProteinLasso: A Lasso regression approach to protein inference problem in shotgun proteomics
Computational Biology and Chemistry
A Combinatorial Perspective of the Protein Inference Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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.