Toward supportive data collection tools for plant metabolomics
Toward supportive data collection tools for plant metabolomics
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DOI：
10.1104/pp.104.058875
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发表时间：
2005-05-01
期刊：
影响因子：
7.4
通讯作者：
Hardy, N
中科院分区：
文献类型：
--
作者：
Jenkins, H;Johnson, H;Hardy, N
近年来，许多倡议提出了功能基因组学实验的标准报告指南。与这些相关的是数据模型，可以用作设计以标准格式存储和传输实验数据的软件工具的基础。这些数据处理工具成功的关键在于它们的可用性。成功的数据处理工具有望在节省时间和保证质量方面带来好处。在这里，我们描述了符合最近提出的植物代谢组学数据模型的数据集，称为ArMet（代谢组学架构），并说明了软件工程师和生物学家合作开发的一些可靠数据收集方法。这些例子还从数据收集的角度验证了ArMet，证明了一系列支持数据记录和数据上传到中央数据库的软件工具可以使用数据模型作为其设计的基础。
Over recent years, a number of initiatives have proposed standard reporting guidelines for functional genomics experiments. Associated with these are data models that may be used as the basis of the design of software tools that store and transmit experiment data in standard formats. Central to the success of such data handling tools is their usability. Successful data handling tools are expected to yield benefits in time saving and in quality assurance. Here, we describe the collection of datasets that conform to the recently proposed data model for plant metabolomics known as ArMet ( architecture for metabolomics) and illustrate a number of approaches to robust data collection that have been developed in collaboration between software engineers and biologists. These examples also serve to validate ArMet from the data collection perspective by demonstrating that a range of software tools, supporting data recording and data upload to central databases, can be built using the data model as the basis of their design.