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New Computing Model Could Lead to Quicker Advancements in Medical Research


Virginia Tech Professor Wu Feng

"We are now analyzing data faster, and we are also analyzing it more intelligently," says Virginia Tech Professor Wu Feng.

Credit: Virginia Tech

Virginia Tech researchers have developed data management and analysis software for data-intensive scientific applications in the cloud that could help speed up medical research. Virginia Tech professor Wu Feng is leading the research, which began in April 2010 when the U.S. National Science Foundation and Microsoft launched a collaborative cloud computing agreement, which ultimately funded 13 projects to help researchers integrate cloud technology into their research. Feng led a team in developing an on-demand, cloud-computing model.

"Our goal was to keep up with the data deluge in the DNA sequencing space," Feng says. "Our result is that we are now analyzing data faster, and we are also analyzing it more intelligently." The model enables researchers worldwide to view the same data sets. "This cooperative cloud computing solution allows life scientists and their institutions easy sharing of public data sets and helps facilitate large-scale collaborative research," Feng says.

Feng's team built on the work by creating SeqInCloud and CloudFlow. SeqInCloud offers a portable cloud solution for next-generation sequence analysis that optimizes data management to improve performance and cloud resource use. Feng says CloudFlow enables the management of workflows, such as SeqInCloud, to "allow the construction of pipelines that simultaneously use the client and the cloud resources for running the pipeline and automating data transfers."

From Virginia Tech
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Abstracts Copyright © 2013 Information Inc., Bethesda, Maryland, USA


 

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