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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: May 30, 2026

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae
15:41

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

Published on: October 12, 2009

Evaluating experimental bias and completeness in comparative phosphoproteomics analysis.

Jos Boekhorst1, Paul J Boersema, Bastiaan B J Tops

  • 1Theoretical Biology and Bioinformatics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, The Netherlands. Jos.Boekhorst@gmail.com

Plos One
|August 20, 2011
PubMed
Summary

Comparative phosphoproteomics is challenged by incomplete data and workflow biases. This study introduces bioinformatics strategies to assess workflow impact, improving analysis of phosphorylation networks.

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Area of Science:

  • Proteomics
  • Systems Biology
  • Bioinformatics

Background:

  • Phosphorylation networks are vital for cellular function.
  • Increased phosphoproteomics data presents analysis challenges due to incomplete coverage and experimental biases.
  • Differentiating true biological variation from missing data in phosphoproteomes is difficult.

Purpose of the Study:

  • To evaluate the impact of incomplete phosphoproteomics datasets on comparative analysis.
  • To develop bioinformatics strategies for quantifying experimental workflow effects on measured phosphoproteomes.
  • To enhance the comparative analysis of phosphorylation data.

Main Methods:

  • Analysis of phosphosite saturation in replicates to assess phosphoproteome coverage.
  • Comparison of datasets from different experimental pipelines against a common reference.
  • Experimental measurement and comparison of tyrosine phosphoproteomes from *C. elegans* and HeLa cells.

Main Results:

  • Phosphosite saturation plots offer a reproducible measure of phosphoproteome extent.
  • Experimental workflow significantly impacts phosphoproteome similarity.
  • Comparative analysis is most robust when datasets share identical experimental workflows.
  • A 4% overlap was found between *C. elegans* and HeLa tyrosine phosphoproteomes, a three-fold increase over older studies using varied workflows.

Conclusions:

  • Bioinformatics strategies can estimate the impact of experimental workflow differences on dataset overlap.
  • Standardized workflows are crucial for powerful comparative phosphoproteomics.
  • These methods enable insights from public phosphorylation data, despite workflow variations.