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Related Concept Videos

Proteomics01:33

Proteomics

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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...
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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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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.
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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Visualization of proteomics data using R and bioconductor.

Laurent Gatto1, Lisa M Breckels, Thomas Naake

  • 1Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge, UK; Department of Biochemistry, Computational Proteomics Unit, University of Cambridge, Cambridge, UK.

Proteomics
|February 19, 2015
PubMed
Summary
This summary is machine-generated.

This study overviews how R software visualizes high-throughput proteomics data. It details R

Keywords:
BioconductorBioinformaticsData analysisProgrammingRVisualization

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Data Science

Background:

  • Data visualization is crucial in high-throughput biology for exploration and communication.
  • Understanding complex proteomics datasets requires effective visualization tools.

Purpose of the Study:

  • To provide an overview of R software's application in visualizing proteomics data.
  • To summarize R's plotting systems for analyzing mass spectrometry-based proteomics data.

Main Methods:

  • Review of R software capabilities for data visualization.
  • Demonstration of plotting systems for raw and processed proteomics data.

Main Results:

  • R offers versatile plotting systems for proteomics data exploration.
  • Visualization aids in understanding data structure and patterns in MS-based proteomics.

Conclusions:

  • R software is a powerful tool for visualizing and communicating proteomics data.
  • Effective visualization enhances the interpretation of complex biological datasets.