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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: Jun 15, 2026

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

Census for proteome quantification.

Sung Kyu Park¹, John R Yates¹

¹The Scripps Research Institute, La Jolla, California.

Current Protocols in Bioinformatics

|March 6, 2010

Summary

This summary is machine-generated.

Census is a new software tool designed for efficient quantitative proteomics. It analyzes mass spectrometry data from various labeling strategies, improving accuracy and providing robust quality control for reliable results.

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Area of Science:

Proteomics
Mass Spectrometry
Bioinformatics

Background:

Quantitative proteomics is crucial but faces challenges due to large datasets and diverse analytical methods.
Efficient and accurate analysis of mass spectrometry data is essential for high-throughput proteomics.

Purpose of the Study:

To introduce Census, a novel software tool for efficient quantitative analysis of high-throughput mass spectrometry data.
To enable robust analysis of various stable isotope labeling and label-free proteomics experiments.

Main Methods:

Census utilizes high-resolution mass spectrometry data with a small accuracy tolerance to minimize interferences.
The software incorporates multiple scoring algorithms (e.g., least-squares correlation, probability score) for peptide quantification.
Built-in statistical filters ensure robust quality control of quantitative proteomics results.

Main Results:

Census efficiently analyzes shotgun proteomics data from diverse quantitative strategies, including SILAC, iTRAQ, TMT, (15)N, (18)O, and label-free methods.
Quantitative accuracy is enhanced by reducing contributions from interfering peaks and chemical noise.
The tool provides comprehensive scoring and statistical filtering for reliable peptide quantification.

Conclusions:

Census offers an efficient and accurate solution for quantitative proteomics data analysis.
The software supports a wide range of labeling techniques, enhancing its applicability in proteomics research.
Census facilitates robust quality control, ensuring the reliability of quantitative proteomics findings.