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Related Experiment Video

Updated: May 3, 2026

Combining Chemical Cross-linking and Mass Spectrometry of Intact Protein Complexes to Study the Architecture of Multi-subunit Protein Assemblies
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Condenser: a statistical aggregation tool for multi-sample quantitative proteomic data from Matrix Science Mascot

Anders Dahl Knudsen1, Tue Bennike2, Henrik Kjeldal3

  • 1Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Gustav Wieds Vej 10C, DK-8000 Aarhus C, Denmark.

Journal of Proteomics
|February 18, 2014
PubMed
Summary

Condenser is a new open-source tool that merges complex proteomics data for easier analysis. It supports various quantitation methods and includes quality control features for reliable protein abundance measurements.

Keywords:
Mascot DistillerProteomicsQuantitationSQLStatistics

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Quantitative proteomics generates large, complex datasets.
  • Integrating data from diverse quantitation methods is challenging.
  • Standardized data processing is crucial for reliable analysis.

Purpose of the Study:

  • To introduce Condenser, an open-source software tool.
  • To facilitate the merging and analysis of multidimensional quantitative proteomics data.
  • To enhance the existing Mascot Distiller workflow.

Main Methods:

  • Condenser merges data from Mascot Distiller Quantitation Toolbox.
  • Supports various relative quantitation techniques (e.g., stable isotope labeling, label-free).
  • Includes options for data curation, normalization, and statistical analysis.

Main Results:

  • Condenser provides a unified format for proteomics data.
  • Offers customizable peptide filtering for data quality control.
  • Enables identification of significantly altered protein abundances through statistical testing.

Conclusions:

  • Condenser simplifies the handling of large, multidimensional proteomics experiments.
  • It is a valuable addition to quantitative proteomics workflows.
  • The tool promotes reproducible and high-quality bioinformatic analysis.