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Ornl Helps Develop Hybrid Computational Strategy For Efficient Sequencing of Massive Genome Datasets


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Oak Ridge National Laboratorys Manjunath Gorentla Venkata.

Researchers at Oak Ridge National Laboratory say they have developed a novel computational strategy that can discover genetic variants efficiently on an unprecedented scale.

Credit: Oak Ridge National Laboratory

Oak Ridge National Laboratory (ORNL) researchers say they have developed a novel hybrid computational strategy to efficiently discover genetic variants on an unprecedented scale.

They analyzed more than 5,000 whole genome-sequenced samples with decades of health information related to common chronic diseases.

The team says the new approach connects powerful computing resources to deliver high-quality sensitive analysis of thousands of genetic samples in a timely manner.

"After multiple discussions, we were convinced that mapping pipeline components based on system architecture strengths and tailoring parameters to the architecture would provide quality analysis with a relatively short turnaround," says ORNL researcher Manjunath Gorentla Venkata. He notes the hybrid pipeline processed more than 5,300 whole genome samples in six weeks and can be scaled to analyze more than 10,000 with the same high sensitivity and specificity.

Venkata says the entire project used 5.2 million core hours and transferred 6 terabytes of data across all of the platforms. The researchers used the Rhea computing cluster at the Oak Ridge Leadership Computing Facility to reconstruct chromosomal segments inherited from parents and to statistically predict the makeup of incomplete or mission genetic sequences from discovered genetic markers.

From Oak Ridge National Laboratory
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Abstracts Copyright © 2016 Information Inc., Bethesda, Maryland, USA


 

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