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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: May 16, 2026

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary

Juergen Cox1, Matthias Mann

  • 1Department for Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany. cox@biochem.mpg.de

BMC Bioinformatics
|November 27, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces 2D annotation enrichment, a novel method for interpreting quantitative proteomics and other omics data. It correlates different data types to understand functional protein changes and biological pathways.

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Related Experiment Videos

Last Updated: May 16, 2026

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Quantitative proteomics yields vast protein abundance data upon perturbations.
  • Interpreting this data functionally and correlating it with other 'omics' data, like transcriptomics, is crucial.
  • Existing methods lack robust approaches for integrating and interpreting multi-omics quantitative datasets.

Purpose of the Study:

  • To introduce a novel computational method, 2D annotation enrichment, for the functional interpretation of quantitative multi-omics data.
  • To enable the correlation of quantitative data from any two 'omics' types, facilitating deeper biological insights.
  • To provide a statistically rigorous framework for identifying biologically relevant patterns within complex datasets.

Main Methods:

  • Developed 2D annotation enrichment to compare quantitative data from any two 'omics' types against categorical annotations (pathways, Gene Ontology terms, etc.).
  • Introduced a two-dimensional generalization of the nonparametric two-sample test for statistical formulation.
  • Implemented stringent control of the false discovery rate through multiple hypothesis testing correction.
  • Also described 1D annotation enrichment for single omics data analysis.

Main Results:

  • The 2D annotation enrichment method effectively detects annotation terms with consistent behavior across two quantitative 'omics' dimensions.
  • Demonstrated the ability to identify correlations or distinct regulation patterns between different data types.
  • The method provides a robust statistical framework for analyzing complex biological data.
  • The algorithms are available within the Perseus software package.

Conclusions:

  • 2D annotation enrichment offers a powerful new approach for the functional interpretation of quantitative proteomics and other omics data.
  • This method facilitates the integration and analysis of multi-omics datasets, leading to enhanced biological understanding.
  • The availability of the algorithms in Perseus promotes wider adoption and application in biological research.