Medical College of Wisconsin
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Low cost, scalable proteomics data analysis using Amazon's cloud computing services and open source search algorithms. J Proteome Res 2009 Jun;8(6):3148-53 PMID: 19358578 PMCID: PMC2691775

Abstract

One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step-by-step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center Web site ( http://proteomics.mcw.edu/vipdac ).

Author List

Halligan BD, Geiger JF, Vallejos AK, Greene AS, Twigger SN

Author

Andrew S. Greene PhD Interim Vice Chair, Chief, Professor in the Biomedical Engineering department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Algorithms
Cluster Analysis
Databases, Protein
Internet
Proteomics
Software



View this publication's entry at the Pubmed website PMID: 19358578
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