'Omics Data Sharing

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Science  09 Oct 2009:
Vol. 326, Issue 5950, pp. 234-236
DOI: 10.1126/science.1180598

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Development of high-throughput genomic and postgenomic technologies has caused a change in approaches to data handling and processing (1). One biological sample might be used to generate many kinds of “big” data in parallel, such as genome sequence (genomics), patterns of gene and protein expression (transcriptomics and proteomics), and metabolite concentrations and fluxes (metabolomics). Extensive computer manipulations are required for even basic analyses of such data; the challenges mount further when two or more studies' outputs must be compared or integrated.

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