Proteome bioinformatics

Author(s)

    • Hubbard, Simon J.
    • Jones, Andrew R.

Bibliographic Information

Proteome bioinformatics

edited by Simon J. Hubbard, Andrew R. Jones

(Methods in molecular biology / John M. Walker, series editor, 604)(Springer protocols)

Humana, c2010

Available at  / 2 libraries

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Note

Includes bibliographical references and index

Description and Table of Contents

Description

The feld of proteomics moves rapidly. New methods, techniques, applications, standards, models and software appear almost on a daily basis. Accompanying this are plenty of texts on the experimental side of the feld and a few appearing on the informatic and data analysis side. This latterly includes one in the Methods in Molecular Biology series tackling the specifc analysis of "Mass spectrometry data in proteomics" in MMB vol. 376. This current collection builds on this, but takes a broader view of proteome data analysis covering data analysis essentials, but also the databases and data models, as well as practical consid- ations for analysing database search results, annotating genomes, and speeding up searches. It also digs deeper into some topics, such as decoy database searching and aspects of signal processing in proteomic mass spectrometry. The aim of the volume is to provide the reader with a mix of reviews and methodology chapters, which build from the essentials of database searching in proteomics, on through specifc data processing challenges to databases, data standards and data models.

Table of Contents

Table of Contents Table of Contents Preface Contributors 1. An Introduction to Proteome Bioinformatics Andrew R. Jones and Simon J. Hubbard 2. Bioinformatics Methods for Protein Identification Using Peptide Mass Fingerprinting Zhao Song, Luonan Chen, and Dong Xu 3. Computational approaches to peptide identification via tandem MS Simon J. Hubbard 4. Scoring and validation of tandem MS peptide identification methods Markus Brosch and Jyoti Choudhary 5. Target-Decoy Search Strategy for Mass Spectrometry-based Proteomics Joshua E. Elias and Steven P. Gygi 6. Understanding and Exploiting Peptide Fragment Ion Intensities Using Experimental and Informatic Approaches Ashley C. Gucinski, Eric D. Dodds, Wenzhou Li, and Vicki H. Wysocki 7. Spectral Library Searching for Peptide Identification via Tandem MS Henry Lam and Ruedi Aebersold 8. De novo Sequencing Methods in Proteomics Christopher Hughes, Bin Ma, and Gilles A. Lajoie 9. Cross Species Proteomics J. C. Wright, R. J. Beynon, and S. J. Hubbard 10. Gene model detection using mass spectrometry Bindu Nanduri, Nan Wang, Mark L. Lawrence, Susan M. Bridges, and Shane C. Burgess 11. Signal Processing in Proteomics Rene Hussong and Andreas Hildebrandt 12. A High-Performance Reconfigurable Computing Solution for Peptide Mass Fingerprinting Daniel Coca and Istvan Bogdan 13. Mining proteomic MS/MS data for MRM transitions Jennifer A. Mead, Luca Bianco, and Conrad Bessant 14. OpenMS and TOPP: Open Source Software for LC-MS Data Analysis Knut Reinert and Oliver Kohlbacher 15. Trans-Proteomic Pipeline: A Pipeline For Proteomic Analysis Patrick G. A. Pedrioli 16. Informatics and statistics for analyzing 2-D gel electrophoresis images Andrew W. Dowsey, Jeffrey S. Morris, Howard B. Gutstein, and Guang-Zhong Yang 17. Automated generic analysis tools for protein quantitation using stable isotope labelling Wen-Lian Hsu and Ting-Yi Sung 18. An overview of label-free quantitation methods in proteomics by mass spectrometry Jason W.H. Wong and Gerard Cagney 19. The PeptideAtlas Project Eric W. Deutsch 20. Using the PRIDE Proteomics Identifications Database for Knowledge Discovery and Data Analysis Philip Jones and Lennart Martens 21. Molecular Interactions and data standardisation Sandra Orchard and Samuel Kerrien 22. Mass Spectrometer Output File Format mzML Eric W. Deutsch 23. Managing experimental data using FuGE Andrew R. Jones and Allyson L. Lister 24. Proteomics Data Collection (ProDaC) - Publishing and Collecting Proteomics Data Sets in Public Repositories using Standard Formats Christian Stephan, Martin Eisenacher, Michael Kohl, and Helmut E. Meyer 25. Computational Resources for the Prediction and Analysis of Native Disorder in Proteins Melissa Pentony, Jonathan Ward, and David Jones

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Details

  • NCID
    BB03966878
  • ISBN
    • 9781607614432
  • Country Code
    us
  • Title Language Code
    eng
  • Text Language Code
    eng
  • Place of Publication
    New York, N.Y
  • Pages/Volumes
    xi, 403 p.
  • Size
    26 cm
  • Parent Bibliography ID
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