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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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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata.

Laurent Gatto1, Lisa M Breckels, Samuel Wieczorek

  • 1Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, CB2 1QR, Cambridge, UK and Université Grenoble-Alpes, CEA (iRSTV/BGE), INSERM (U1038), CNRS (FR3425), 38054 Grenoble, France.

Bioinformatics (Oxford, England)
|January 14, 2014
PubMed
Summary
This summary is machine-generated.

pRoloc offers a computational infrastructure for analyzing spatial proteomics data. This tool enhances protein classification and identifies new sub-cellular locations using machine learning.

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Area of Science:

  • Proteomics
  • Computational Biology
  • Cellular Biology

Background:

  • Spatial proteomics aims to map proteins to sub-cellular compartments using quantitative proteomics.
  • Advancements in this field require robust computational tools for data analysis.

Purpose of the Study:

  • To present pRoloc, a comprehensive infrastructure for the analysis of spatial proteomics data.
  • To provide tools for data exploration, protein classification, and novelty detection.

Main Methods:

  • Development of pRoloc, an infrastructure for spatial proteomics data analysis.
  • Implementation of unsupervised and supervised machine learning algorithms.
  • Integration with existing data management and processing systems.

Main Results:

  • pRoloc provides functionality for sound analysis of quantitative mass-spectrometry-based spatial proteomics data.
  • The infrastructure supports data exploration, protein classification, and novelty detection.
  • Enables identification of new putative sub-cellular protein clusters.

Conclusions:

  • pRoloc offers a complete solution for spatial proteomics data analysis.
  • Facilitates deeper insights into biological processes through improved protein localization.
  • Supports the advancement of experimental spatial proteomics research.