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Supercomputing for the parallelization of whole genome analysis.

Megan J Puckelwartz1, Lorenzo L Pesce1, Viswateja Nelakuditi1

  • 1Department of Medicine, Computation Institute and Argonne National Laboratory, 9700 S. Cass Ave. Argonne, IL 60439, USA, Department of Human Genetics, The University of Chicago, 5841 S. Maryland Ave Chicago, IL 60637, USA, Department of Internal Medicine, The University of Michigan, 1150 W Medical Center Dr. Ann Arbor, MI 48109, USA, Perelman School of Medicine, Penn Cardiovascular Institute and Department of Medicine, University of Pennsylvania, 3400 Civic Center Blvd. Philadelphia, PA 19104, USA and Washington University School of Medicine, 660 S. Euclid Ave. St. Louis, MO 63110, USA.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

Researchers optimized whole genome sequencing analysis using a supercomputer, enabling faster processing of multiple genomes. This advancement significantly reduces computational time and increases usable sequence data for large-scale genomic studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Declining DNA sequencing costs drive an increase in whole genome sequencing (WGS) applications.
  • Analyzing multiple human genomes simultaneously presents a significant computational bottleneck.

Purpose of the Study:

  • To adapt supercomputing resources for parallelized, concurrent multiple genome analysis.
  • To overcome computational limitations in large-scale WGS data processing.

Main Methods:

  • Utilized the Cray XE6 supercomputer at Argonne National Laboratory.
  • Developed the MegaSeq workflow to leverage supercomputer architecture for WGS.
  • Employed publicly available software for alignment and variant calling.

Main Results:

  • Achieved parallelization for concurrent multiple genome analysis.
  • Significantly reduced computational time for WGS data processing.
  • Demonstrated capacity to align and call variants on 240 whole genomes in approximately 50 hours.
  • Accelerated multisample variant calling.

Conclusions:

  • Supercomputer adaptation enables efficient, large-scale WGS analysis.
  • The MegaSeq workflow enhances throughput and usability of genomic data.
  • This approach addresses the computational bottleneck in analyzing numerous genomes concurrently.