'Omics Data Sharing

Data sharing, and the good annotation practices it depends on, must become part of the fabric of daily research for researchers and funders. 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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