HPCwire: A Genomic CluE for Cloud Computing

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April 24, 2009

A Genomic CluE for Cloud Computing

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COLLEGE PARK, Md., April 23 -- DNA sequencing is the next frontier in biological research. As new sequencing technology becomes more efficient and affordable, it is increasingly available to small laboratories. Thus, sequencing data is being generated at a faster rate than ever before.

However, the computing capacity needed to analyze such vast amounts of data still has some catching up to do. Large networks of interconnected computers, called computer clusters, are required to analyze these data. Expensive to establish and maintain, these computer clusters are generally available only to labs that can afford them.

Enter Mihai Pop, an assistant professor in the department of computer science and in the Center for Bioinformatics and Computational Biology at the University of Maryland. He and colleague Steven Salzberg, director of the center and Horvitz Professor of computer science, recently received a grant from the National Science Foundation Cluster Exploratory Program (CluE) to fund research aimed at discovering how remote cluster computers, computer networks available over the Internet, might be used to process DNA sequence data.

"There is a new initiative by NSF to figure out what you can do with cluster computers on the internet -- like the ones through Amazon, Google, and IBM," Pop said. "Our NSF grant will be used to find out if remote clusters of computers are a better option for DNA sequence analysis than local clusters of computers."

Pop's goal is to develop the software required to analyze sequence data in parallel (on many computers simultaneously). This massively parallel computing allows faster gene sequence alignment and genome assembly.

While parallel computing is already being used on locally maintained computer clusters, Pop will be working on programs that will allow researchers to perform their DNA sequence over the web by accessing remote computer clusters maintained by large companies on a pay-per-use basis. This paradigm is known as cloud computing.

So now, rather than buying and maintaining their own computer systems, researchers may simply be able to rent computer time at a fraction of the cost. But there are a few obstacles to overcome before Cloud Computing becomes a reality for genetic analysts.

"The first question is how to best split up the process of DNA sequence analysis to fit these computer clusters," Pop said. "The second is whether or not the benefits of cloud computing outweigh the costs of data transfer and storage."

The massive amounts of data generated by just one genome may take a significant amount of time to transfer over the internet. This, in addition to the data storage needed before analysis, might add costs that outweigh the benefits of using a remote computer cluster.

"Even if the analysis doesn't take long, the transfer may take forever and cost too much to make whole thing worthwhile," said Pop.

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