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Quantitative Proteomics by Mass Spectrometry

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Cover of 'Quantitative Proteomics by Mass Spectrometry'

Table of Contents

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    Book Overview
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    Chapter 1 Increased Depth and Breadth of Plasma Protein Quantitation via Two-Dimensional Liquid Chromatography/Multiple Reaction Monitoring-Mass Spectrometry with Labeled Peptide Standards.
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    Chapter 2 Quantitative Analysis of the Sirt5-Regulated Lysine Succinylation Proteome in Mammalian Cells.
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    Chapter 3 Determining the Composition and Stability of Protein Complexes Using an Integrated Label-Free and Stable Isotope Labeling Strategy.
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    Chapter 4 Label-Free Quantitation for Clinical Proteomics.
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    Chapter 5 Proteogenomic Methods to Improve Genome Annotation
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    Chapter 6 Mass Spectrometry-Based Quantitative O-GlcNAcomic Analysis.
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    Chapter 7 Isolating and Quantifying Plasma HDL Proteins by Sequential Density Gradient Ultracentrifugation and Targeted Proteomics.
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    Chapter 8 A Method for Label-Free, Differential Top-Down Proteomics. - PubMed - NCBI
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    Chapter 9 Multiplexed Immunoaffinity Enrichment of Peptides with Anti-peptide Antibodies and Quantification by Stable Isotope Dilution Multiple Reaction Monitoring Mass Spectrometry.
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    Chapter 10 High-Throughput Quantitative Proteomics Enabled by Mass Defect-Based 12-Plex DiLeu Isobaric Tags
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    Chapter 11 Isotopic N,N-Dimethyl Leucine (iDiLeu) for Absolute Quantification of Peptides Using a Standard Curve Approach.
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    Chapter 12 Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards.
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    Chapter 13 Using the CPTAC Assay Portal to Identify and Implement Highly Characterized Targeted Proteomics Assays
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    Chapter 14 Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC-MS/MS
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    Chapter 15 Multiple and Selective Reaction Monitoring Using Triple Quadrupole Mass Spectrometer: Preclinical Large Cohort Analysis.
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    Chapter 16 Methods for SWATH™: Data Independent Acquisition on TripleTOF Mass Spectrometers. - PubMed - NCBI
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    Chapter 17 Measurement of Phosphorylated Peptides with Absolute Quantification.
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    Chapter 18 Proteomic Analysis of Protein Turnover by Metabolic Whole Rodent Pulse-Chase Isotopic Labeling and Shotgun Mass Spectrometry Analysis
Attention for Chapter 12: Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards.
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Chapter title
Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards.
Chapter number 12
Book title
Quantitative Proteomics by Mass Spectrometry
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3524-6_12
Pubmed ID
26867746
Book ISBNs
978-1-4939-3522-2, 978-1-4939-3524-6
Authors

James G. Bollinger, Andrew B. Stergachis, Richard S. Johnson, Jarrett D. Egertson, Michael J. MacCoss

Editors

Salvatore Sechi

Abstract

In targeted proteomics, the development of robust methodologies is dependent upon the selection of a set of optimal peptides for each protein-of-interest. Unfortunately, predicting which peptides and respective product ion transitions provide the greatest signal-to-noise ratio in a particular assay matrix is complicated. Using in vitro synthesized proteins as analytical standards, we report here an empirically driven method for the selection of said peptides in a human plasma assay matrix.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 24%
Student > Ph. D. Student 6 21%
Student > Bachelor 2 7%
Student > Master 2 7%
Professor > Associate Professor 2 7%
Other 2 7%
Unknown 8 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 31%
Neuroscience 2 7%
Engineering 2 7%
Computer Science 2 7%
Chemistry 2 7%
Other 2 7%
Unknown 10 34%

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