Increasingly, genome-wide association studies are being used to identify positions within the human genome that have a link with a disease condition. The number of genomic locations studied means that computationally intensive and bioinformatic intensive solutions will have to be used in the analysis of these data sets. In this paper we present an integrated Workbench that provides user-friendly access to parallelized statistical genetics analysis codes for clinical researchers. In addition we biologically annotate statistical analysis results through the reuse of existing bionformatic Taverna workflows.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041536 | PMC |
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