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Related Concept Videos

Proteomics01:33

Proteomics

7.5K
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...
7.5K

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Updated: May 3, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Implementation of statistical process control for proteomic experiments via LC MS/MS.

Michael S Bereman1, Richard Johnson, James Bollinger

  • 1Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA, michaelbereman@ncsu.edu.

Journal of the American Society for Mass Spectrometry
|February 6, 2014
PubMed
Summary
This summary is machine-generated.

Statistical Process Control in Proteomics (SProCoP) offers real-time quality control for proteomic workflows using Skyline software. This tool enhances data reliability by monitoring key performance metrics without requiring peptide identification.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Statistical Process Control (SPC) is crucial for process monitoring and variation identification.
  • Proteomic workflows generate complex data requiring robust quality control.
  • Existing methods may lack real-time, integrated performance evaluation.

Purpose of the Study:

  • To introduce Statistical Process Control in Proteomics (SProCoP), a novel tool for quality control in proteomics.
  • To integrate SPC principles, including control charts and Pareto analysis, into the Skyline software.
  • To provide real-time monitoring of critical performance metrics in proteomic experiments.

Main Methods:

  • Development of SProCoP using the R statistical language, integrated with Skyline software.
  • Monitoring of five key quality control metrics: retention time reproducibility, peak asymmetry, resolution, targeted peptide ion intensity, and mass measurement accuracy.
  • Empirical determination of experiment- and instrument-specific thresholds using quality control standards.
  • Utilization of control charts for real-time performance evaluation and Pareto analysis for variance summarization.

Main Results:

  • SProCoP enables real-time evaluation of chromatographic and mass spectrometric performance without peptide identification.
  • The tool effectively separates random noise from systematic error by setting empirical thresholds.
  • Pareto analysis highlights metrics with high variance, guiding users to areas needing attention.
  • Case studies demonstrate the utility of SProCoP in assessing proteomic experiment quality.

Conclusions:

  • SProCoP provides a valuable, integrated solution for real-time quality control in shotgun and targeted proteomics.
  • The tool enhances the reliability and interpretability of proteomic data by monitoring critical performance metrics.
  • SProCoP facilitates the identification of process variations, leading to improved experimental outcomes and data quality.