Monte carlo simulation-based algorithms for analysis of shotgun proteomic data.
Two new statistical models based on Monte Carlo Simulation (MCS) have been developed to score peptide matches in shotgun proteomic data and incorporated in a database search program, MassMatrix (www.massmatrix.net). The first model evaluates peptide matches based on the total abundance of matched peaks in the experimental spectra. The second model evaluates amino acid residue tags within MS/MS spectra. The two models provide complementary scores for peptide matches that result in higher confidence in peptide identification when significant scores are returned from both models. The MCS-based models use a variance reduction technique that improves estimation precision. Due to the high computational expense of MCS-based models, peptide matches were prefiltered by other statistical models before further evaluation by the MCS-based models. Receiver operating characteristic analysis of the data sets confirmed that MCS-based models improved the overall performance of the MassMatrix search software, especially for low-mass accuracy data sets.
Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, Ohio 43210, USA.
Hua
H
Xu
Michael A
MA
Freitas
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D016208:Databases, Factual; D004560:Electricity; D015233:Models, Statistical; D009010:Monte Carlo Method; D010455:Peptides / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D053719:Tandem Mass Spectrometry
D000596:Amino Acids; D010455:Peptides