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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,...

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

Published on: October 19, 2021

Knowledge-based analysis of proteomics data.

Marina Bessarabova1, Alexander Ishkin, Lellean JeBailey

  • 1Thomson Reuters, IP & Science, 5901 Priestly Dr., #200, Carlsbad, CA 92008, USA.

BMC Bioinformatics
|November 27, 2012
PubMed
Summary

Interpreting proteomics data requires functional analysis. This study presents knowledge-based methods using the MetaBase knowledge source for enhanced proteomics data interpretation in various diseases.

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Area of Science:

  • Proteomics
  • Systems Biology
  • Bioinformatics

Background:

  • The value of proteomics data hinges on functional interpretation within a phenotypic context.
  • Analyzing complex proteomics profiles involves understanding protein pathways, interactions, and regulatory networks.

Purpose of the Study:

  • To describe knowledge-based methods for functional interpretation of proteomics data.
  • To demonstrate the application of these methods using the MetaBase knowledge source across different disease areas.

Main Methods:

  • Utilizing ontology enrichment, interactome topology, and network analysis.
  • Applying these methods to a comprehensive, manually curated knowledge source (MetaBase).
  • Comparing proteomics profiles with structured databases of protein interactions, pathways, and disease associations.

Main Results:

  • Demonstration of functional interpretation of proteomics profiles through case studies.
  • Successful application of knowledge-based approaches in diverse disease contexts.
  • Enhanced understanding of proteomics data through integrated network and pathway analysis.

Conclusions:

  • Knowledge-based approaches, leveraging curated databases like MetaBase, are crucial for complex proteomics data interpretation.
  • Ontology enrichment and network analysis provide powerful tools for linking proteomics to biological function and disease.
  • This methodology facilitates a deeper understanding of biological systems and disease mechanisms from proteomics studies.