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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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Protein Networks02:26

Protein Networks

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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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Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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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Bioinformatics Resources for Interpreting Proteomics Mass Spectrometry Data.

Iulia M Lazar1

  • 1Department of Biological Sciences, Virginia Tech, Integrated Life Sciences Building (ILSB), Room 2011, 1981 Kraft Drive, Blacksburg, VA, 24061, USA. malazar@vt.edu.

Methods in Molecular Biology (Clifton, N.J.)
|August 16, 2017
PubMed
Summary
This summary is machine-generated.

Mass spectrometry (MS) advancements aid proteomic research for disease insights. This guide simplifies MS data processing and analysis for biological context, aiding drug discovery and personalized medicine.

Keywords:
BioinformaticsData interpretationMass spectrometryProteomics

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Area of Science:

  • Proteomics
  • Biotechnology
  • Bioinformatics

Background:

  • Mass spectrometry (MS) advancements enable proteomic analysis for distinguishing healthy and diseased states.
  • Proteomic insights facilitate novel drug target identification, therapeutic development, and personalized medicine diagnostics.
  • Large-scale proteomic data complexity necessitates sophisticated bioinformatics tools for interpretation.

Purpose of the Study:

  • To provide a roadmap for mass spectrometry data acquisition and processing.
  • To describe common bioinformatics tools for interpreting proteomic data.
  • To guide researchers in placing mass spectrometry results into biological context.

Main Methods:

  • Data acquisition strategies in mass spectrometry.
  • Proteomic data processing workflows.
  • Bioinformatics tool selection for data analysis.

Main Results:

  • A structured approach to mass spectrometry data handling.
  • Overview of commonly used bioinformatics resources.
  • Guidance on biological interpretation of proteomic findings.

Conclusions:

  • Effective MS data processing and analysis are crucial for advancing proteomic research.
  • Accessible bioinformatics tools can demystify complex proteomic datasets.
  • This chapter serves as a guide for researchers navigating MS data analysis for biological insights.