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Proteomics

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...

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Updated: Apr 22, 2026

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

Published on: October 12, 2009

Data analysis techniques in phosphoproteomics.

Anke Meyer-Baese¹, Joachim Wildberger, Uwe Meyer-Baese

¹Department of Scientific Computing, Florida State University, FL, USA.

Electrophoresis

|October 15, 2014

Summary

This summary is machine-generated.

Interpreting phosphoproteomics data is key for new therapies. This paper reviews two main techniques for analyzing these large datasets, aiding in discovering therapeutic targets.

Keywords:

Dynamical modeling Exploratory data analysis Partial least square regression Phosphoproteomics Topology-driven methods

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Last Updated: Apr 22, 2026

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

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A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors

A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors

Published on: April 29, 2022

Area of Science:

Proteomics
Systems Biology
Bioinformatics

Background:

Phosphoproteomics data interpretation is vital for hypothesis generation in therapeutic development.
Limited techniques are currently available for analyzing large-scale phosphoproteomics datasets.
Understanding protein interactions is crucial for disease research and drug discovery.

Purpose of the Study:

To provide an overview of standard techniques for phosphoproteomics data analysis.
To compare data-driven and topology-driven analytical approaches.
To highlight the importance of these methods in identifying therapeutic targets.

Main Methods:

Overview of data-driven (exploratory, statistical model-based) techniques.
Overview of topology-driven methods (dynamical analysis of signaling networks).
Discussion of how these algorithms reveal proteome-level interactions.

Main Results:

Both data-driven and topology-driven methods offer unique insights into phosphoproteomics data.
These techniques can identify distinct "fingerprints" within complex biological systems.
The analysis reveals intricate interactions at the proteome level.

Conclusions:

Standardized analysis techniques are essential for advancing phosphoproteomics research.
These methods support the experimental environment for developing novel therapeutics.
Effective interpretation of phosphoproteomics data can accelerate the discovery of treatments for various diseases.