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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 5, 2026

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry

Published on: March 13, 2014

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free

Hyungwon Choi¹, Sinae Kim², Damian Fermin³

¹Saw Swee Hock School of Public Health, National University of Singapore.

Journal of Proteomics

|August 9, 2015

Summary

This summary is machine-generated.

We introduce QPROT, a new computational tool for analyzing protein expression data. This framework enhances quantitative proteomics by effectively handling missing intensity data and improving differential expression analysis.

Keywords:

Continuously normalized spectral counts Differential expression Intensity Missing data

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Last Updated: Apr 5, 2026

A New Approach for the Comparative Analysis of Multiprotein Complexes Based on 15N Metabolic Labeling and Quantitative Mass Spectrometry

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Published on: March 13, 2014

Quantitative Analysis of Chromatin Proteomes in Disease

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Published on: December 28, 2012

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Published on: April 19, 2019

Area of Science:

Proteomics
Computational Biology
Statistical Analysis

Background:

Quantitative proteomics relies on accurate differential expression analysis.
Existing methods may not adequately handle missing data in protein intensity measurements.
Adaptation of spectral count data analysis tools for intensity data is needed.

Purpose of the Study:

To introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data.
To extend the QSPEC suite for handling continuously measured protein-level intensity data.
To provide a robust method for analyzing label-free quantitative proteomics data.

Main Methods:

QPROT utilizes a novel intensity normalization procedure.
It employs model-based differential expression analysis that accounts for missing data.
Differential expression is determined using a Z-statistic based on the posterior distribution of log fold change, with FDR estimated by Empirical Bayes.

Main Results:

QPROT was evaluated using CPTAC and Escherichia coli benchmark datasets.
The framework demonstrated classification performance in these evaluations.
Accuracy of False Discovery Rate (FDR) was assessed on the E. coli dataset.

Conclusions:

QPROT is a comprehensive statistical framework and software tool for comparative quantitative proteomics.
It offers probabilistic treatment of missing values, extending the QSPEC method for intensity data.
The tool is expected to be highly valuable for proteomics laboratories utilizing label-free quantitative data.