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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...

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Low cost, scalable proteomics data analysis using Amazon's cloud computing services and open source search

Brian D Halligan1, Joey F Geiger, Andrew K Vallejos

  • 1Biotechnology and Bioengineering Center, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, Wisconsin 53226, USA. Halligan@mcw.edu

Journal of Proteome Research
|April 11, 2009
PubMed
Summary
This summary is machine-generated.

This study presents a cloud-based system for proteomics data analysis, enabling scalable virtual clusters without hardware costs. It offers a cost-effective solution for researchers needing powerful computational resources for proteomics.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Establishing computational infrastructure for proteomics data analysis is a significant challenge for many laboratories.
  • High costs associated with hardware acquisition and software licensing hinder proteomics program development.

Purpose of the Study:

  • To describe a novel system for creating scalable virtual proteomics analysis clusters.
  • To enable laboratories to perform large-scale proteomics data analysis without substantial upfront investment.

Main Methods:

  • Utilizes distributed cloud computing (e.g., Amazon Web Services) and open-source software.
  • Provides detailed instructions for implementing virtual proteomics analysis clusters.
  • Offers preconfigured Amazon machine images with OMSSA and X!Tandem search algorithms and databases.

Main Results:

  • Demonstrates a cost-effective method for accessing large-scale computational resources for proteomics.
  • Eliminates the need for significant investment in computational hardware and software licenses.
  • Facilitates the setup of scalable virtual analysis clusters.

Conclusions:

  • The described system lowers the barrier to entry for proteomics research by providing accessible and affordable computational power.
  • Laboratories and individual researchers can leverage cloud resources for efficient proteomics data analysis.
  • The availability of preconfigured resources simplifies the implementation process.