Gel-Free Proteomics Methods and Protocols
Proteomics by means of mass spectrometry has rapidly changed the way that we analyze proteomes. Gel-Free Proteomics: Methods and Protocols addresses contemporary methods for gel-free proteome research with a special focus on differential analysis and prot
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Abbreviations: FDR, False discovery rate; m/z, Mass-to-charge ratio; MS, Mass spectrometry; MS/MS, Tandem-MS; SRM, Selected reaction monitoring;
1. Introduction The field of mass spectrometry-based proteomics has matured quite considerably over the past 10 years. Fueled by substantial advances in instrumentation (1), sequence databases (2, 3), specialized software (4), and innovative methodologies (5), the field has quickly transformed into a high-throughput analytical tool for the identification and quantification of hundreds to thousands of proteins per experiment. This ability to generate continuously large volumes of information has correspondingly increased the pressure on the downstream data processing algorithms and pipelines. Furthermore, the innovative techniques used in sample preparation have brought several lingering issues in proteomics K. Gevaert, J. Vandekerckhove (eds.), Gel-Free Proteomics, Methods in Molecular Biology 753, DOI 10.1007/978-1-61779-148-2_24, © Springer Science+Business Media, LLC 2011
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data processing into sharper contrast. Indeed, the data processing now quite often provides a considerable bottleneck in proteomics experiments, with the development of robust algorithms and production-grade software to address the management and interpretation of the acquired data lagging behind developments in the other areas of proteomics research. This chapter therefore aims to outline the various challenges and issues in data processing that can be encountered when performing a typical proteomics experiment. The following sections follow the overall workflow in proteomics analyses, starting immediately after the acquisition of raw mass spectral data by the mass spectrometer. Each subsequent stage of data processing will then be individually discussed in detail, moving from the conversion of the raw data signal to peaks, over the identification of peptides from these peak lists and the inference of proteins from these peptides, to the quantification of the proteins. Finally, the informatics aspects relevant to the fast developing field of targeted proteomics using selected reaction monitoring will also be discussed.
2. From Raw Data to Peaks There is a consistent confusion in the field as to the meaning of the term raw data, so it is important to explicitly state here what is meant when reference is made to raw data. Raw data is here considered to be the proprietary, binary output provided by the instrument at the end of an analysis (6). Very few downstream approaches actually use the raw data directly, however. The sheer size of the data precludes fast access, and the level of detail stored in these files can overwhelm downstream algorithms. The typical first step in data processing is therefore the use of so-called signal processing algorithms to analyze and reduce the raw data to obtain a much more manageable set of peaks instead (7). Despite the significant size reduction typically obtained by this type of processing (6), possible errors are relatively small (7). It is im
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