Statistical analysis of mass spectrometry-assisted protein identification methods
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Mass spectrometry combined with database search utilities is a valuable protein identification tool. The success of database mining is dependent upon a series of variables such as mass accuracy, protein purity, peptide yield, and the genomic complexity of the target organism as well as the size of database searched. Ten proteins are selected to quantify the dynamic interaction of the variables of interest using statistical methods. The ten proteins’ spectra and simulated randomized spectra were searched using two searching programs. Statistically significant effects due to variation in mass accuracy, peptide coverage, level of impurity, database size and their interactions are found and quantified.