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Answering the demands of digital genomics

Authors

  • Matthew A. Titmus,

    1. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
    2. Department of Molecular and Cellular Biology, Stony Brook University, Stony Brook, NY, USA
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  • James Gurtowski,

    1. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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  • Michael C. Schatz

    Corresponding author
    1. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
    2. Department of Molecular and Cellular Biology, Stony Brook University, Stony Brook, NY, USA
    • Correspondence to: Michael C. Schatz, Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.

      E-mail: mschatz@cshl.edu

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SUMMARY

The continuing revolution in DNA sequencing and biological sensor technologies is driving a digital transformation to our approaches for observation, experimentation, and interpretation that form the foundation of modern biology and genomics. Whereas classical experiments were limited to thousands of hand-collected observations, today's improved sensors allow billions of digital observations and are improving at an exponential rate that exceeds Moore's law. These improvements have made it possible to monitor the dynamics of biological processes on an unprecedented scale, but have proportionally greater quantitative and computational requirements.

The exponentially growing digital demands have motivated extensive research into improved algorithms and parallel systems. Recently, a great deal of research has been focused on applying emerging scalable computing systems to genomic research. One of the most promising is the Hadoop open-source implementation of MapReduce: it is specifically designed to scale to very large datasets, its intuitive design supports rich parallel algorithms, and is naturally applied to analysis of many biological assays. There has also been success accelerating numerically intensive genomics applications using heterogeneous processors such as GPUs and FPGAs. These are promising early results, but it is clear that continued computational research will become even more important in the years to come. Copyright © 2012 John Wiley & Sons, Ltd.

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