Thomas Craig E, Ganji Gopinath
Toxicology and Drug Disposition, Lilly Research Laboratories, Division of Eli Lilly & Co, PO Box 708, Greenfield, IN 46140, USA.
Curr Opin Drug Discov Devel. 2006 Jan;9(1):92-100.
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.
对基因、蛋白质和代谢物的测量作为研究生物体对刺激（无论是环境、遗传、药理、毒理等方面的刺激）反应的一种手段，已越来越被人们所接受。这些方法通常分别被称为基因组学、蛋白质组学和代谢组学，作为独立的实体，它们无疑提供了十年前无法获得的新的生物学见解。毫不奇怪，科学家们继续拓展边界以从数据中提取知识，目前人们认识到，这些技术的充分实现受到缺乏数据整合工具的限制。整合这些“组学”数据集，即整合组学，是很有必要的，因为它将各个生物元素联系在一起，以便更全面地理解动态生物过程。因此，除了开发新的数据分析方法以分别从每个高含量数据集中提取更多细节外，人们还在努力创建或改进统计方法、数据库、注释和通路映射，以最大限度地增进我们的了解。最近在哺乳动物和非哺乳动物系统中都有几个例子，其中基因、蛋白质和/或代谢物已通过生物学或数据驱动策略进行了整合。在此，对关键发现进行综述，识别并举例说明我们当前工具和技术中的差距，并展望整合组学在生物学研究中的潜力。
