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Related Concept Videos

Proteomics01:33

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 9, 2026

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures
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ProtQuant: a tool for the label-free quantification of MudPIT proteomics data.

Susan M Bridges1, G Bryce Magee, Nan Wang

  • 1Department of Computer Science and Engineering, Mississippi State University, Starkville, MS 39762, USA. bridges@cse.msstate.edu

BMC Bioinformatics
|December 6, 2007
PubMed
Summary

ProtQuant is a new tool for label-free protein quantification using mass spectrometry data. It improves upon the SigmaXCorr method by introducing SigmaXCorr* to handle missing data, reducing false positives in differential expression analysis.

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Area of Science:

  • Proteomics
  • Computational Biology
  • Biotechnology

Background:

  • Quantitative analysis of high-throughput mass spectrometry data is crucial for cell state characterization.
  • Label-free methods, including SigmaXCorr, are used for differential protein expression analysis in MudPIT experiments.
  • Limited availability of user-friendly tools for label-free protein quantification hinders widespread adoption.

Purpose of the Study:

  • To introduce ProtQuant, a Java-based tool for label-free protein quantification from MudPIT datasets.
  • To implement the SigmaXCorr method with an improved approach for handling missing data (SigmaXCorr*).
  • To provide a user-friendly and portable solution for bench scientists.

Main Methods:

  • ProtQuant utilizes the SigmaXCorr method for label-free protein quantification.
  • A novel algorithm, SigmaXCorr*, is implemented to address missing data by incorporating 'below threshold' peptide scores.
  • The tool is designed with a graphical user interface, supports multiple file formats, and handles large datasets and numerous replicates/treatments.

Main Results:

  • ProtQuant offers ease of use and portability for bench scientists.
  • The SigmaXCorr* algorithm demonstrates an average 25% reduction in false positive identifications of differential expression compared to SigmaXCorr.
  • The tool efficiently processes protein datasets generated with Sequest.

Conclusions:

  • ProtQuant is a versatile, multi-platform tool for label-free protein quantification.
  • It provides an intuitive interface and essential data management/analysis features for Sequest-generated datasets.
  • ProtQuant's availability as a self-installing executable for Windows enhances accessibility for many researchers.