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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...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Related Experiment Video

Updated: May 25, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

How advancement in biological network analysis methods empowers proteomics.

Wilson W B Goh1, Yie H Lee, Maxey Chung

  • 1Department of Computing, Imperial College London, UK.

Proteomics
|January 17, 2012
PubMed
Summary
This summary is machine-generated.

Proteomics offers unique biological insights but faces coverage and consistency challenges. Network-based analysis, when carefully applied, enhances data interpretation and addresses these critical proteomics issues.

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

Published on: November 15, 2017

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Last Updated: May 25, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

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:

  • Biochemistry and Molecular Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Proteomics provides crucial biological and disease insights not available from genomics (RNA/DNA).
  • Current proteomics technologies struggle with incomplete data coverage and inter-sample inconsistency.
  • Network-based analysis methods offer potential solutions but require careful implementation.

Purpose of the Study:

  • To review current proteomics technologies and identify causes of data coverage and consistency issues.
  • To propose network-based strategies for improving proteomics data robustness and interpretation.
  • To highlight the value of integrated network and pathway analysis in quantitative proteomics.

Main Methods:

  • Review of current proteomics technologies and data analysis challenges.
  • Discussion of holistic, network-based approaches for addressing data coverage.
  • Exploration of group-based and pathway database integration for enhancing data consistency.

Main Results:

  • Holistic analysis using biological networks provides a framework for robust proteomics models.
  • Network-based methods and integrated pathway databases improve data consistency.
  • Despite limitations, network analysis yields valuable insights complementing quantitative proteomics.

Conclusions:

  • Network-based analysis is essential for overcoming critical proteomics limitations.
  • Careful application of network methods enhances the interpretation of proteomics data.
  • Integrated biological networks and pathway databases significantly augment quantitative proteomics findings.