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Modern Proteomics – Sample Preparation, Analysis and Practical Applications

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Cover of 'Modern Proteomics – Sample Preparation, Analysis and Practical Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Proteomes, Their Compositions and Their Sources
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    Chapter 2 Protein Fractionation and Enrichment Prior to Proteomics Sample Preparation
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    Chapter 3 Sample Preparation for Mass Spectrometry-Based Proteomics; from Proteomes to Peptides
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    Chapter 4 Plant Structure and Specificity – Challenges and Sample Preparation Considerations for Proteomics
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    Chapter 5 Improving Proteome Coverage by Reducing Sample Complexity via Chromatography
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    Chapter 6 Database Search Engines: Paradigms, Challenges and Solutions
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    Chapter 7 Mass Analyzers and Mass Spectrometers
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    Chapter 8 Top-Down Mass Spectrometry: Proteomics to Proteoforms
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    Chapter 9 Platforms and Pipelines for Proteomics Data Analysis and Management
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    Chapter 10 Tandem Mass Spectrum Sequencing: An Alternative to Database Search Engines in Shotgun Proteomics
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    Chapter 11 Visualization, Inspection and Interpretation of Shotgun Proteomics Identification Results
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    Chapter 12 Protein Inference
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    Chapter 13 Modification Site Localization in Peptides
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    Chapter 14 Useful Web Resources
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    Chapter 15 Mass Spectrometry-Based Protein Quantification
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    Chapter 16 Bioinformatics Tools for Proteomics Data Interpretation
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    Chapter 17 Identification, Quantification, and Site Localization of Protein Posttranslational Modifications via Mass Spectrometry-Based Proteomics
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    Chapter 18 Protein-Protein Interaction Detection Via Mass Spectrometry-Based Proteomics
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    Chapter 19 Protein Structural Analysis via Mass Spectrometry-Based Proteomics
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    Chapter 20 Introduction to Clinical Proteomics
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    Chapter 21 Discovery of Candidate Biomarkers
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    Chapter 22 Statistical Approaches to Candidate Biomarker Panel Selection
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    Chapter 23 Qualification and Verification of Protein Biomarker Candidates
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    Chapter 24 Protocol for Standardizing High-to-Moderate Abundance Protein Biomarker Assessments Through an MRM-with-Standard-Peptides Quantitative Approach
Attention for Chapter 13: Modification Site Localization in Peptides
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Chapter title
Modification Site Localization in Peptides
Chapter number 13
Book title
Modern Proteomics – Sample Preparation, Analysis and Practical Applications
Published in
Advances in experimental medicine and biology, December 2016
DOI 10.1007/978-3-319-41448-5_13
Pubmed ID
Book ISBNs
978-3-31-941446-1, 978-3-31-941448-5
Authors

Robert J. Chalkley

Editors

Hamid Mirzaei, Martin Carrasco

Abstract

There are a large number of search engines designed to take mass spectrometry fragmentation spectra and match them to peptides from proteins in a database. These peptides could be unmodified, but they could also bear modifications that were added biologically or during sample preparation. As a measure of reliability for the peptide identification, software normally calculates how likely a given quality of match could have been achieved at random, most commonly through the use of target-decoy database searching (Elias and Gygi, Nat Methods 4(3): 207-214, 2007). Matching the correct peptide but with the wrong modification localization is not a random match, so results with this error will normally still be assessed as reliable identifications by the search engine. Hence, an extra step is required to determine site localization reliability, and the software approaches to measure this are the subject of this part of the chapter.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 25%
Student > Bachelor 1 13%
Professor 1 13%
Student > Ph. D. Student 1 13%
Student > Master 1 13%
Other 0 0%
Unknown 2 25%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 2 25%
Biochemistry, Genetics and Molecular Biology 2 25%
Computer Science 1 13%
Medicine and Dentistry 1 13%
Engineering 1 13%
Other 0 0%
Unknown 1 13%