Review

Summary

Computational solutions to large-scale data management and analysis. Schadt, Eric E., Linderman, Michael D., Sorenson, Jon, Lee, Lawrence and Nolan, Garry P.. Nat Rev Genet, 11(9):647-657, 09 2010/09//print. (URL)

Abstract

Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist $(G!7(B such as cloud and heterogeneous computing $(G!7(B to successfully tackle our big data problems

Bibtex entry

@ARTICLE { Review,
    ANNOTE = { 10.1038/nrg2857 },
    AUTHOR = { Schadt, Eric E. and Linderman, Michael D. and Sorenson, Jon and Lee, Lawrence and Nolan, Garry P. },
    ADDED = { 2011-03-14 14:04:40 +0100 },
    MODIFIED = { 2011-03-14 14:04:40 +0100 },
    ISBN = { 1471-0056 },
    JOURNAL = { Nat Rev Genet },
    M3 = { 10.1038/nrg2857 },
    MONTH = { 09 },
    NUMBER = { 9 },
    PAGES = { 647--657 },
    PUBLISHER = { Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. },
    TITLE = { Computational solutions to large-scale data management and analysis },
    TY = { JOUR },
    URL = { http://dx.doi.org/10.1038/nrg2857 },
    VOLUME = { 11 },
    YEAR = { 2010/09//print },
    ABSTRACT = { Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist $(G!7(B such as cloud and heterogeneous computing $(G!7(B to successfully tackle our big data problems },
    1 = { http://dx.doi.org/10.1038/nrg2857 },
}