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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: May 7, 2026

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Published on: August 17, 2015

Is Protein Quantification and Physical Normalization Always Necessary in Proteomics?

Alex Zelter¹, Michael Riffle¹, Gennifer E Merrihew¹

¹Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA.

Biorxiv : the Preprint Server for Biology

|February 23, 2026

Summary

This summary is machine-generated.

Protein quantification is often considered essential for liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) proteomics. This study demonstrates that omitting physical normalization can save time and costs without significantly impacting results after computational normalization.

Keywords:

BCA Mass spectrometry normalization protein proteomics quantitative

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Last Updated: May 7, 2026

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Area of Science:

Biochemistry
Analytical Chemistry
Proteomics

Background:

Traditional proteomics workflows mandate protein quantification and physical normalization.
These steps add significant time, cost, and complexity, particularly for large-scale studies.
Computational normalization strategies are standard in proteomics data analysis.

Purpose of the Study:

To investigate the impact of omitting physical protein normalization on quantitative proteomics data.
To determine if computational normalization can compensate for the absence of physical normalization.
To identify potential time and cost savings in proteomics workflows.

Main Methods:

Comparison of quantitative proteomics data from samples with and without physical protein amount normalization.
Analysis of peptide and protein abundance variations.
Application of standard computational normalization techniques to both normalized and unnormalized datasets.

Main Results:

Increased variation in input protein amounts correlated with higher variance in output peptide and protein abundances.
Omitting physical normalization did not lead to unacceptable increases in measurement variability when computational normalization was applied.
The study established a relationship between input variation and output variance in quantitative proteomics.

Conclusions:

Protein quantification and physical normalization may be omitted in certain quantitative proteomics experiments.
Computational normalization effectively compensates for the lack of physical normalization in many cases.
This finding enables significant time and cost optimizations for proteomics workflows.