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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 proteomics...

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Related Experiment Video

Updated: Jul 5, 2026

Quantitative Analysis of Chromatin Proteomes in Disease

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

A computational strategy to analyze label-free temporal bottom-up proteomics data.

Xiuxia Du¹, Stephen J Callister, Nathan P Manes

¹Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, USA.

Journal of Proteome Research

|April 30, 2008

Summary

Understanding dynamic protein abundances is crucial for biological systems. This study presents a new strategy to analyze temporal proteomics data, addressing challenges like missing values and identifying significant patterns in Rhodobacter sphaeroides.

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Last Updated: Jul 5, 2026

Quantitative Analysis of Chromatin Proteomes in Disease

Quantitative Analysis of Chromatin Proteomes in Disease

Published on: December 28, 2012

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Published on: October 24, 2018

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

Proteomics and Systems Biology
Microbial Physiology

Background:

Biological systems exhibit constant change, requiring analysis of dynamic protein abundances.
Advancements in mass spectrometry-based quantitative proteomics enable the study of protein abundance dynamics.
Extracting meaningful biological insights from dynamic proteomics data faces challenges including variability, missing values, and pattern identification.

Purpose of the Study:

To present a novel strategy for analyzing temporal bottom-up proteomics data.
To address key challenges in dynamic proteomics data analysis.
To demonstrate the strategy's utility using a Rhodobacter sphaeroides time-course dataset.

Main Methods:

Development of a data analysis strategy tailored for temporal proteomics.
Application of the strategy to bottom-up proteomics data.
Utilizing time-course data from Rhodobacter sphaeroides 2.4.1.

Main Results:

The proposed strategy effectively handles extraneous variability and missing abundance values.
Significant temporal patterns in protein abundances were successfully identified.
The strategy proved valuable for analyzing the Rhodobacter sphaeroides time-course study.

Conclusions:

The developed strategy offers a robust approach to analyzing dynamic proteomics data.
This method enhances the extraction of biological information from temporal proteomics studies.
The findings provide a framework for future investigations into dynamic biological systems.