The cost of obtaining genome-scale biomedical data continues to drop rapidly, with many hospitals and uni- versities being able to produce large amounts of data. Managing and analysing such ever-growing datasets is becoming a crucial issue. Cloud computing presents a good solution to this problem due to its flexibility in obtaining computational resources. However, it is essential to allow end-users with no experience to take advantage of the cloud computing model of elastic resource provisioning. This paper presents a workflow that allows the end-user to perform the core steps of a genome wide association analysis where raw gene- expression data is quality assessed. A number of steps in this process are computationally intensive and vary greatly depending on the size of the study, from a few samples to a few thousand. Therefore cloud computing provides an ideal solution to this problem by enabling scalability due to elastic resource provisioning. The key contributions of this paper are a real world application of cloud computing addressing a critical problem in biomedicine through parallelization of the appropriate parts of the workflow as well as enabling the end-user to concentrate on data analysis and biological interpretation of results by taking care of the computational aspects. Copyright © 2014 SCITEPRESS - Science and Technology Publications.
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