Abstract

The measurement of genes, proteins and metabolites has gained increasing acceptance as a means by which to study the response of an organism to stimuli, whether they are environmental, genetic, pharmacological, toxicological, etc. Typically referred to as genomics, proteomics, and metabonomics or metabolomics, respectively, these methods as independent entities have undoubtedly provided new biological insight that was not attainable a decade ago. Not surprisingly, scientists continue to push the boundaries to extract knowledge from data, and it is currently recognized that the full realization of these technologies is limited by a lack of tools to enable data integration. Integration of these 'omic datasets, or integromics, is desirable as it links the individual biological elements together to provide a more complete understanding of dynamic biological processes. Accordingly, in addition to developing new data analysis methods to extract further details from each of the high-content datasets individually, effort is also being expended to create or improve statistical methods, databases, annotations and pathway mapping to maximize our learning. There are several recent examples, in both mammalian and non-mammalian systems, in which genes, proteins and/or metabolites have been integrated using either biology- or data-driven strategies. Herein, key findings are reviewed, gaps in our current tools and technologies are identified and illustrated, and perspective is provided on the potential of integromics in biological research.