Erik Gafni

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In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and(More)
SUMMARY Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it(More)
The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. ABSTRACT Summary: Efficient workflows to shepherd clinically generated gen-omic data through the multiple stages of a next-generation sequen-cing pipeline are of critical importance in translational biomedical science. Here we present(More)
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