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Author:   Simon J. Hubbard ,  Andrew R. Jones
Publisher:   Humana Press Inc.
Imprint:   Humana Press Inc.
Edition:   2010 ed.
Volume:   604
Dimensions:   Width: 17.80cm , Height: 2.30cm , Length: 25.40cm
Weight:   2.070kg
ISBN:  

9781607614432

ISBN 10:   160761443
Pages:   404
Publication Date:   23 December 2009
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Awaiting stock  
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Table of Contents

An Introduction to Proteome Bioinformatics.- Bioinformatics Methods for Protein Identification Using Peptide Mass Fingerprinting.- Computational Approaches to Peptide Identification via Tandem MS.- Scoring and Validation of Tandem MS Peptide Identification Methods.- Target-Decoy Search Strategy for Mass Spectrometry-Based Proteomics.- Understanding and Exploiting Peptide Fragment Ion Intensities Using Experimental and Informatic Approaches.- Spectral Library Searching for Peptide Identification via Tandem MS.- De Novo Sequencing Methods in Proteomics.- Cross Species Proteomics.- Gene Model Detection Using Mass Spectrometry.- Signal Processing in Proteomics.- A High-Performance Reconfigurable Computing Solution for Peptide Mass Fingerprinting.- Mining Proteomic MS/MS Data for MRM Transitions.- OpenMS and TOPP: Open Source Software for LC-MS Data Analysis.- Trans-Proteomic Pipeline: A Pipeline for Proteomic Analysis.- Informatics and Statistics for Analyzing 2-D Gel Electrophoresis Images.- Automated Generic Analysis Tools for Protein Quantitation Using Stable Isotope Labeling.- An Overview of Label-Free Quantitation Methods in Proteomics by Mass Spectrometry.- The PeptideAtlas Project.- Using the PRIDE Proteomics Identifications Database for Knowledge Discovery and Data Analysis.- Molecular Interactions and Data Standardisation.- Mass Spectrometer Output File Format mzML.- Managing Experimental Data Using FuGE.- Proteomics Data Collection (ProDaC): Publishing and Collecting Proteomics Data Sets in Public Repositories Using Standard Formats.- Computational Resources for the Prediction and Analysis of Native Disorder in Proteins.

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