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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...
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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

Shaping biological knowledge: applications in proteomics.

F Lisacek1, C Chichester, P Gonnet

  • 1R&D GeneBio, 25 Avenue de Champel, Geneva 1206, Switzerland. frederique.lisacek@genebio.com

Comparative and Functional Genomics
|July 17, 2008
PubMed
Summary

Integrating genomics and proteomics data is challenging due to complex protein interactions. This study assesses bioinformatics resource biases in small-scale proteomics studies, offering complements to biological ontologies.

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Proteomics

Background:

  • The central dogma guides genomics data integration, but proteomics integration lacks clear principles due to poorly understood protein folding and interactions.
  • Current bioinformatics resources may introduce biases and approximations in integrating protein data.

Purpose of the Study:

  • To assess biases in bioinformatics resources for proteomics data integration.
  • To explore methods for integrating disparate protein information into a biologically meaningful framework.
  • To develop specialized complements for classical biological ontologies.

Main Methods:

  • Analysis of proteomics data using a data-driven approach, focusing on proteins smaller than 10 kDa.
  • Application of a hypothesis-driven approach, examining whole bacterial proteomes.
  • Evaluation of bioinformatics resource biases in small-scale studies.

Main Results:

  • Identified biases and approximations in bioinformatics resources used for proteomics data integration.
  • Demonstrated the utility of both data-driven and hypothesis-driven approaches in analyzing proteomics data.
  • Highlighted the potential for specialized complements to existing biological ontologies.

Conclusions:

  • Bioinformatics resource biases can impact proteomics data integration.
  • Both data-driven and hypothesis-driven analyses are valuable for understanding proteomes.
  • The study provides insights for enhancing biological ontologies with proteomics data.