Global open data management in metabolomics.
Global open data management in metabolomics.
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DOI：
10.1016/j.cbpa.2016.12.024
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发表时间：
2017-02
影响因子：
7.8
通讯作者：
Steinbeck C
中科院分区：
文献类型：
--
作者：
Haug K;Salek RM;Steinbeck C
代谢组允许以动态的方式获得生物体存在和发展的外部影响。近年来，代谢组学数据交换的全球网络已经建立起来。全球代谢组学数据交换正在导致可用于重新分析的公开代谢组学数据呈指数级增长。化学生物学运用化学合成、分析化学等工具研究生物系统。分子生物学的最新进展，如下一代测序（NGS），使人们对生物生化谱的进化有了前所未有的认识。由于基因组学中特定的数据共享文化，来自所有生命王国的基因组很容易被其他研究人员进一步分析。基因组表达了生物体适应外部影响的潜力，而代谢组则呈现了一种分子表型，使我们能够评估生物体以动态方式存在和发展的外部影响。仪器向高通量和高分辨率方法的稳步发展，导致了用于测量和分析生物体代谢组的分析化学方法的复兴。代谢组学作为一个领域的稳定增长正在导致世界各地实验室的大数据积累，这在所有其他组学领域都可以观察到。这就要求开发方法和技术来处理和处理如此大的数据集，以便有效地分发它们，并使重新分析成为可能。在这里，我们描述了最近出现的全球开放存取数据库生态系统和它们之间的数据交换努力，以及支持或阻止数据共享和重新分析这些数据的基础和障碍。
The metabolome allows accessing the external influences under which an organism exists and develops in a dynamic way. Recent years have seen the establishment of a global network for metabolomics data exchange. Global metabolomics data exchange is leading to an exponential growth of publically available metabolomics data for re-analysis. Chemical Biology employs chemical synthesis, analytical chemistry and other tools to study biological systems. Recent advances in both molecular biology such as next generation sequencing (NGS) have led to unprecedented insights towards the evolution of organisms’ biochemical repertoires. Because of the specific data sharing culture in Genomics, genomes from all kingdoms of life become readily available for further analysis by other researchers. While the genome expresses the potential of an organism to adapt to external influences, the Metabolome presents a molecular phenotype that allows us to asses the external influences under which an organism exists and develops in a dynamic way. Steady advancements in instrumentation towards high-throughput and highresolution methods have led to a revival of analytical chemistry methods for the measurement and analysis of the metabolome of organisms. This steady growth of metabolomics as a field is leading to a similar accumulation of big data across laboratories worldwide as can be observed in all of the other omics areas. This calls for the development of methods and technologies for handling and dealing with such large datasets, for efficiently distributing them and for enabling re-analysis. Here we describe the recently emerging ecosystem of global open-access databases and data exchange efforts between them, as well as the foundations and obstacles that enable or prevent the data sharing and reanalysis of this data.