Abstract
Recent advances in high throughput technologies analysing biological samples enabled the researchers to collect a huge amount of data. In particular, mass spectrometry-based proteomics uses the mass spectrometry to investigate proteins expressed in an organism or a cell. The manual inspection of spectra is unfeasible, so the need to introduce a set of algorithms, tools and platforms to manage and analyze them arises. Computational Proteomics regards the computational methods for analyzing spectra data in qualitative (i.e. peptide/protein identification in tandem mass spectrometry), and quantitative proteomics (i.e. protein expression in samples), as well as in biomarker discovery (i.e. the identification of a molecular signature of a disease directly from spectra). This chapter presents main standards, tools, and technologies for building scalable, reusable, and portable applications in this field. The chapter surveys available solutions for computational proteomics and includes a deep description of MS-Analyzer, a Grid-based software platform for the integrated management and analysis of spectra data. MS-Analyzer provides efficient spectra management through a specialized spectra database, and supports the semantic composition of pre-processing and data mining services to analyze spectra on the Grid.
TopBackground
Recent developments in high throughput technologies have enabled the study of the cell on a large scale. The resulting scenario is characterized by the birth of new disciplines leading to the start of a new era, the so-called omics era. The suffix omics identifies a discipline that analyzes the relation of various biological compounds in their environment. For instance, proteomics is the study of the proteins expressed in a cell (Aebersold and Mann, 2003).
Mass Spectrometry (MS) has become a core technology in proteomics. The application of techniques based on mass spectrometry to analyze samples has a big impact in understanding both qualitative and quantitative distribution of proteins in a sample and finally to individuate potential biomarkers (Listgarten et al., 2005; Petricoin et al., 2002; Morris et al., 2005) .
Key Terms in this Chapter
Computational Proteomics: Regards the use of computational science methods and tool to extract information from proteomics data.
LC-MS/MS (Liquid Chromatography- Tandem Mass Spectrometry): The combined use of Liquid Chromatography coupled to two mass-analyzer. In the first stage a ion, namely precursor ion is selected and fragmented. In the second stage these fragments are analyzed. This techniques enable in such a way the identification of proteins in a sample starting from the constitutive peptides analyzed in the second stage.
Peptide/Protein Identification: The process that aims to identify the proteins in a biological sample analyzed using MS. There exists many identification algorithms that can be grouped in: (i) database-based method, that match a real spectra with a database of theoretic ones; and (ii) de novo sequencing that aims to infer the presence of unknown proteins in a sample.
Biomarker Discovery: The process to identify a possible molecular signature for a disease of a specific condition starting from proteomics data.
LC-MS (Liquid Chromatography-Mass Spectrometry): The combined use of a chromatographic technique (LC) followed by a mass-spectometer. The former enables the separation of substances analyzed by MS improving the quality of overall process.
Proteomics: The study of the proteome, i.e. the whole set of proteins expressed in a cell or in a tissue.
Mass Spectrometry (MS): An analytical technique that aimis to identifies the chemical composition of a compound. In proteomics MS is used to identify the proteins contained in a sample, providing also some quantitative information, i.e. the relative abundance.
MALDI-TOF (Matrix Assisted Laser/Desorption Ionization – Time of Flight): Mass Spectrometry Is a kind of spectrometer. Information gained from using MALDI-TOF MS are mainly qualitative, e.g. the presence of a protein. Main advantage of such spectrometry relies in the relative.
Peptide/Protein Quantitation: The process of the calculation of the relative abundance of peptides and proteins. It relies mainly on the kind of technology used to preprocess samples. There exist two possible approaches: (i) label based methods that pre-process sample with specific reagents, and (ii) label-free method that do not use labels.