Computational solutions to large-scale data management and analysis

Eric E. Schadt, Michael D. Linderman, Jon Sorenson, Lawrence Lee, Garry P. Nolan

Research output: Contribution to journalReview articlepeer-review

432 Scopus citations

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

Original languageEnglish
Pages (from-to)647-657
Number of pages11
JournalNature Reviews Genetics
Volume11
Issue number9
DOIs
StatePublished - Sep 2010
Externally publishedYes

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