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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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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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MSDA, a proteomics software suite for in-depth Mass Spectrometry Data Analysis using grid computing.

Christine Carapito1, Alexandre Burel, Patrick Guterl

  • 1Laboratoire de Spectrométrie de Masse BioOrganique, IPHC, Université de Strasbourg, CNRS, UMR7178, Strasbourg, France.

Proteomics
|March 29, 2014
PubMed
Summary

The Mass Spectrometry Data Analysis (MSDA) software suite addresses computational bottlenecks in proteomics by offering tools for in-depth MS/MS data analysis. MSDA significantly accelerates data processing, achieving up to 99% time savings for complex proteomic datasets.

Keywords:
BioinformaticsComputational proteomicsDatabase searchingDe novo sequencingFunctional annotationsGrid computing

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Area of Science:

  • Proteomics
  • Computational Biology
  • Bioinformatics

Background:

  • High-throughput mass spectrometry (MS) generates massive datasets, posing computational challenges for data interpretation in proteomics.
  • Increasing size and complexity of MS/MS datasets hinder comprehensive processing and biological information extraction.

Purpose of the Study:

  • To present the Mass Spectrometry Data Analysis (MSDA) online software suite for advanced MS/MS data interpretation.
  • To provide a comprehensive solution for processing large-scale proteomic datasets efficiently.

Main Methods:

  • MSDA offers modules for custom database generation, high-quality spectra filtering and extraction.
  • Includes tools for high-performance database and de novo searches, and extraction of modified peptides spectra.
  • Leverages computer grids for computationally intensive tasks like database and de novo searches.

Main Results:

  • MSDA provides a suite of tools for in-depth analysis of MS/MS data.
  • Enables efficient extraction of biological information from complex proteomic datasets.
  • Achieves significant time savings, up to 99%, for computationally intensive search steps via grid computing.

Conclusions:

  • The MSDA software suite effectively overcomes computational bottlenecks in proteomics data analysis.
  • MSDA facilitates comprehensive processing and biological interpretation of large-scale MS/MS datasets.
  • Grid computing integration in MSDA drastically reduces data processing time.