Low cost, scalable proteomics data analysis using Amazon's cloud computing services and open source search algorithms

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.