Data Mining in Proteomics

1. Front Matter

   Pages i-xii

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2. Data Generation and Result Finding

   1. Front Matter

      Pages 1-1

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   2. Instruments and Methods in Proteomics

      • Caroline May, Frederic Brosseron, Piotr Chartowski, Cornelia Schumbrutzki, Bodo Schoenebeck, Katrin Marcus

      Pages 3-26

   3. In-Depth Protein Characterization by Mass Spectrometry

      • Daniel Chamrad, Gerhard Körting, Martin Blüggel

      Pages 27-40

   4. Analysis of Phosphoproteomics Data

      • Christoph Schaab

      Pages 41-57

3. Databases

   1. Front Matter

      Pages 59-59

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   2. The Origin and Early Reception of Sequence Databases

      • Joel B. Hagen

      Pages 61-77

   3. Laboratory Data and Sample Management for Proteomics

      • Jari Häkkinen, Fredrik Levander

      Pages 79-92

   4. PRIDE and “Database on Demand” as Valuable Tools for Computational Proteomics

      • Juan Antonio Vizcaíno, Florian Reisinger, Richard Côté, Lennart Martens

      Pages 93-105

   5. Analysing Proteomics Identifications in the Context of Functional and Structural Protein Annotation: Integrating Annotation Using PICR, DAS, and BioMart

      • Philip Jones

      Pages 107-121

   6. Tranche Distributed Repository and ProteomeCommons.org

      • Bryan E. Smith, James A. Hill, Mark A. Gjukich, Philip C. Andrews

      Pages 123-145

4. Standards

   1. Front Matter

      Pages 147-147

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   2. Data Standardization by the HUPO-PSI: How has the Community Benefitted?

      • Sandra Orchard, Henning Hermjakob

      Pages 149-160

   3. mzIdentML: An Open Community-Built Standard Format for the Results of Proteomics Spectrum Identification Algorithms

      • Martin Eisenacher

      Pages 161-177

   4. Spectra, Chromatograms, Metadata: mzML-The Standard Data Format for Mass Spectrometer Output

      • Michael Turewicz, Eric W. Deutsch

      Pages 179-203

   5. imzML: Imaging Mass Spectrometry Markup Language: A Common Data Format for Mass Spectrometry Imaging

      • Andreas Römpp, Thorsten Schramm, Alfons Hester, Ivo Klinkert, Jean-Pierre Both, Ron M. A. Heeren et al.

      Pages 205-224

   6. Tandem Mass Spectrometry Spectral Libraries and Library Searching

      • Eric W. Deutsch

      Pages 225-232

5. Processing and Interpretation of Data

   1. Front Matter

      Pages 233-233

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   2. Inter-Lab Proteomics: Data Mining in Collaborative Projects on the Basis of the HUPO Brain Proteome Project’s Pilot Studies

      • Michael Hamacher, Bernd Gröttrup, Martin Eisenacher, Katrin Marcus, Young Mok Park, Helmut E. Meyer et al.

      Pages 235-246

   3. Data Management and Data Integration in the HUPO Plasma Proteome Project

      • Gilbert S. Omenn

      Pages 247-257

Through the rapid development of proteomics methods and technologies, an enormous amount of data was created, leading to a wide-spread rethinking of strategy design and data interpretation. In Data Mining in Proteomics: From Standards to Applications, experts in the field present these new insights within the proteomics community, taking the historical evolution as well as the most important international standardization projects into account. Along with basic and sophisticated overviews of proteomics technologies, standard data formats, and databases, the volume features chapters on data interpretation strategies including statistics, spectra interpretation, and analysis environments as well as specialized tasks such as data annotation, peak picking, phosphoproteomics, spectrum libraries, LC/MS imaging, and splice isoforms. As a part of the highly successful Methods in Molecular Biology™ series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Authoritative and cutting-edge, Data Mining in Proteomics: From Standards to Applications is a well-balanced compendium for beginners and experts, offering a broad scope of data mining topics but always focusing on the current state-of-the-art and beyond.

• Data generation & handling
• Data interpretation
• International standardization projects
• Laboratory databases
• Modeling
• Public repositories
• Spectra interpretation
• Statistics
• Visualization

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)

• Lead Discovery Center GmbH, Dortmund, Germany

  Michael Hamacher

• Medizinisches Proteom-Center, Ruhr-Universität Bochum, Bochum, Germany

  Martin Eisenacher, Christian Stephan

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