Computational solutions to large-scale data management and analysis

被引：384

作者：

Schadt, Eric E. ^{[1
]}

Linderman, Michael D. ^{[2
]}

Sorenson, Jon ^{[1
]}

Lee, Lawrence ^{[1
]}

Nolan, Garry P. ^{[3
]}

机构：

[1] Pacific Biosci, Menlo Pk, CA 94025 USA

[2] Stanford Univ, Comp Syst Lab, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Microbiol & Immunol, Stanford, CA 94305 USA

来源：

NATURE REVIEWS GENETICS | 2010年 / 11卷 / 09期

关键词：

MOLECULAR DYNAMIC SIMULATION; GRAPHICS PROCESSING UNITS; DISEASE; TIME; NETWORKS; CLOUD;

D O I：

10.1038/nrg2857

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

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-such as cloud and heterogeneous computing-to successfully tackle our big data problems.

引用

页码：647 / 657

页数：11

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