Journal
NATURE REVIEWS GENETICS
Volume 11, Issue 9, Pages 647-657Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nrg2857
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Funding
- NATIONAL CANCER INSTITUTE [R01CA130826] Funding Source: NIH RePORTER
- NCI NIH HHS [R01 CA130826, R01 CA130826-04] Funding Source: Medline
- NHLBI NIH HHS [HHSN268201000034C] Funding Source: Medline
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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.
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