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Related Experiment Video

Updated: Jun 6, 2026

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

Published on: October 12, 2009

Analysis of phosphoproteomics data.

Christoph Schaab¹

¹Kinaxo Biotechnologies GmbH, Martinsried, Germany. schaab@biochem.mpg.de

Methods in Molecular Biology (Clifton, N.J.)

|November 11, 2010

Summary

Protein phosphorylation is key to cell signaling and diseases like cancer. New statistical methods for phosphoproteomics data analysis are needed and applied to real data.

Area of Science:

Biochemistry
Molecular Biology
Systems Biology

Background:

Protein phosphorylation is a critical regulatory mechanism in cellular signaling pathways.
Dysregulation of these pathways is implicated in diseases such as cancer and inflammation.
Phosphoproteomics offers an unbiased, quantitative approach to study phosphorylation.

Purpose of the Study:

To review key concepts in phosphoproteomics.
To discuss major data analysis methods for phosphoproteomic data.
To apply and evaluate these methods on a real-world dataset.

Main Methods:

Mass spectrometry-based phosphoproteomics.
Statistical analysis of quantitative phosphoproteomic data.
Adaptation and enhancement of methods from gene expression analysis.

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Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer

Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer

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Last Updated: Jun 6, 2026

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

Published on: October 12, 2009

Phosphoproteomic Strategy for Profiling Osmotic Stress Signaling in Arabidopsis

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Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer

Phosphopeptide Enrichment Coupled with Label-free Quantitative Mass Spectrometry to Investigate the Phosphoproteome in Prostate Cancer

Published on: August 2, 2018

Main Results:

Demonstrated the utility of phosphoproteomics in understanding signaling pathways.
Highlighted the need for specialized statistical approaches beyond standard gene expression analysis.
Successfully applied data analysis tools to a relevant biological dataset.

Conclusions:

Phosphoproteomics is a powerful tool for dissecting cellular signaling.
Advanced and tailored statistical methods are essential for robust phosphoproteomic data analysis.
The study provides a framework for analyzing phosphoproteomic data in disease contexts.