Data preprocessing and filtering in mass spectrometry based proteomics
Mass spectrometry based proteomics analysis can produce many thousands of spectra in a single experiment, and much of this data, frequently greater than 50%, cannot be properly evaluated computationally. Therefore a number of strategies have been developed to aid the processing of mass spectra and typically focus on the identification and elimination of noise, which can provide an immediate improvement in the analysis of large data streams. This is mostly carried out with proprietary software. Here we review the current main principles underlying the preprocessing of mass spectrometry data give an overview of the publicly available tools.