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.
Bottom-up proteomics (mass spectrometry analysis of peptides obtained by proteolysis and separated by liquid chromatography, (LCMS/MS)) is one of the most frequently used techniques for identifying and characterizing proteins in biological samples. A key element of the analysis is database searching when the mass spectra of the peptides are compared with a database of theoretically computed (or experimental) peptide spectra. Here we discuss the main computational approaches to spectrum database searching and the statistical analysis of the results
Identification and elimination of noise peaks in mass spectra from large proteomics data streams simultaneously improves the accuracy of peptide identification and significantly decreases the size of the data. There are a number of peak filtering strategies that can achieve this goal. Here we present a simple algorithm wherein the number of highest intensity peaks retained for further analysis is proportional to the mass of the precursor ion.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.