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Systematic Evaluation of Reference Protein Normalization in Proteomic Experiments

Overview of attention for article published in Frontiers in Plant Science, January 2013
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Title
Systematic Evaluation of Reference Protein Normalization in Proteomic Experiments
Published in
Frontiers in Plant Science, January 2013
DOI 10.3389/fpls.2013.00025
Pubmed ID
Authors

Henrik Zauber, Vivian Schüler, Waltraud Schulze

Abstract

Quantitative comparative analyses of protein abundances using peptide ion intensities and their modifications have become a widely used technique in studying various biological questions. In the past years, several methods for quantitative proteomics were established using stable-isotope labeling and label-free approaches. We systematically evaluated the application of reference protein normalization (RPN) for proteomic experiments using a high mass accuracy LC-MS/MS platform. In RPN all sample peptide intensities were normalized to an average protein intensity of a spiked reference protein. The main advantage of this method is that it avoids fraction of total based relative analysis of proteomic data, which is often very much dependent on sample complexity. We could show that reference protein ion intensity sums are sufficiently reproducible to ensure a reliable normalization. We validated the RPN strategy by analyzing changes in protein abundances induced by nutrient starvation in Arabidopsis thaliana. Beyond that, we provide a principle guideline for determining optimal combination of sample protein and reference protein load on individual LC-MS/MS systems.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 2%
Sweden 1 2%
Unknown 42 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 30%
Researcher 7 16%
Student > Master 6 14%
Student > Bachelor 5 11%
Student > Doctoral Student 3 7%
Other 7 16%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 45%
Biochemistry, Genetics and Molecular Biology 8 18%
Computer Science 2 5%
Engineering 2 5%
Chemistry 2 5%
Other 6 14%
Unknown 4 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 February 2013.
All research outputs
#20,184,694
of 22,699,621 outputs
Outputs from Frontiers in Plant Science
#15,808
of 19,910 outputs
Outputs of similar age
#248,720
of 280,695 outputs
Outputs of similar age from Frontiers in Plant Science
#241
of 517 outputs
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