Data Mining in Proteomics

• Instruments and Methods in Proteomics.- In-Depth Protein Characterization by Mass Spectrometry.- Analysis of Phosphoproteomics Data.- The Origin and Early Reception of Sequence Databases.- Laboratory Data and Sample Management for Proteomics.- PRIDE and ‘Database on Demand’ as Valuable Tools for Computational Proteomics.- Analyzing Proteomics Identifications in the Context of Functional and Structural Protein Annotation: Integrating Annotation Using PICR, DAS, and BioMart.- Tranche Distributed Repository and ProteomeCommons.org.- Data Standardization by the HUPO-PSI: How Has the Community Benefitted?.- mzIdentML: An Open Community-Built Standard Format for the Results of Proteomics Spectrum Identification Algorithms.- Spectra, Chromatograms, Metadata: mzML – The Standard Data Format for Mass Spectrometer Output.- imzML: Imaging Mass Spectrometry Markup Language – A Common Data Format for Mass Spectrometry Imaging.- Tandem Mass Spectrometry Spectral Libraries and Library Searching.- Inter-Lab Proteomics: Data Mining in Collaborative Projects on the Basis of the HUPO Brain Proteome Project´s Pilot Studies.- Data Management and Data Integration in the HUPO Plasma Proteome Project.- Statistics in Experimental Design, Preprocessing, and Analysis of Proteomics Data.- The Evolution of Protein Interaction Networks.- Cytoscape: Software for Visualization and Analysis of Biological Networks.- Text Mining for Systems Modelling.- Identification of Alternatively Spliced Transcripts Using a Proteomic Informatics Approach.- Distributions of Ion Series in ETD and CID Spectra: Making a Comparison.- Evaluation of Peak Picking Algorithms for Protein Mass Spectrometry.- OpenMS and TOPP: Open Source Software for LC-MS Data Analysis.- LC/MS Data Processing for Label-Free Quantitative Analysis.- Spectral Properties of Correlation Matrices: Towards Enhanced Spectral Clustering.- Standards, Databases, and Modeling Tools in Systems Biology.- Modelling of Cellular Processes:Methods, Data, and Requirements.

From the reviews:“The field of data mining in proteomics is currently extremely dynamic while encompassing a wide range of specialties, topics and implications, and so is this book. … This is a very interesting compendium of theoretical, almost philosophical contributions next to requirements and tools with suggestions for their integration. Overall the book is a valuable aid for beginners and for experts as well; moreover, it provides some stimulating materials for discussion in an open perspective.” (Molecular Biotechnology, February, 2012)