1. Low Cost, Scalable Proteomics Data Analysis Using Amazon’s Cloud Computing Services and Open Source Search Algorithms
- Author
-
Brian D. Halligan, Andrew S. Greene, Simon N. Twigger, Joey F. Geiger, and Andrew Vallejos
- Subjects
Proteomics ,Structure (mathematical logic) ,Internet ,Database ,Amazon rainforest ,business.industry ,Computer science ,Cloud computing ,General Chemistry ,computer.software_genre ,Biochemistry ,Article ,Set (abstract data type) ,World Wide Web ,ComputingMethodologies_PATTERNRECOGNITION ,Software ,Search algorithm ,Scalability ,Cluster Analysis ,The Internet ,Databases, Protein ,business ,computer ,Algorithms - 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 ).
- Published
- 2009
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