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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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Related Experiment Video

Updated: Nov 6, 2025

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

Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data.

Karin Schork^1,2, Katharina Podwojski^1,3, Michael Turewicz^1,2

¹Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany.

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

|May 5, 2021

Summary

Statistical analysis is crucial for quantitative proteomics to identify differentially regulated proteins. Proper experimental design and robust statistical methods are essential for accurate detection of protein changes.

Keywords:

Data preprocessing Experimental design Fold change Multiple testing Normalization Sample size calculation Statistical hypothesis test Volcano plot

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Last Updated: Nov 6, 2025

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Area of Science:

Proteomics
Statistical Analysis
Bioinformatics

Background:

Mass spectrometry is a key technique in quantitative proteomics for identifying differentially expressed proteins.
Statistical analysis is often overlooked but critical for interpreting complex proteomic data.
Proteomic datasets are typically high-dimensional, necessitating advanced statistical approaches.

Purpose of the Study:

To highlight the importance of statistical analysis in quantitative proteomics.
To emphasize the necessity of proper experimental design for reliable statistical outcomes.
To provide guidance on both the planning and analysis stages of proteomic experiments.

Main Methods:

Discussion of statistical methodologies applicable to high-dimensional proteomic data.
Emphasis on principles of experimental design tailored for quantitative proteomics.
Overview of statistical analysis techniques for detecting differential protein expression.

Main Results:

Correct experimental design is fundamental for successful statistical analysis.
Appropriate statistical methods are required to handle the complexity of proteomic data.
Effective analysis enables the reliable detection of truly differential proteins.

Conclusions:

Integrating statistical planning and analysis is vital for robust quantitative proteomics.
Neglecting statistical aspects can compromise the validity of proteomic findings.
This chapter offers a comprehensive approach to statistical considerations in proteomics.