The development of a database for metabolomics - looking back on ten years of experience

B. Ravenzwaay, H. Kamp, G. Montoya-Parra, V. Strauss, E. Fabian, W. Mellert, G. Krennrich, T. Walk, E. Peter, R. Looser, M. Herold
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引用次数: 15

Abstract

Metabolome profiles of ca. 750 compounds obtained from blood samples from 28 day rat studies (OECD 407) were combined with toxicity profiles in one database over ten years to predict toxicity of new compounds. We provide detailed descriptions of procedures and recommendations for 'omics data-bases. Control of variability (biological, sampling/storage and technical measurement) is essential. At the start of large scale projects control variability should be extensively investigated. Reference (positive control) substances should be used to evaluate and obtain a good signal/noise ratio. Procedures should be documented in standard operating procedures and followed meticulously. Exact repeats of positive controls should be regularly performed, to assess variability of positive responses. Control data should be regularly checked for shifts and analysed to obtain information concerning normality. If possible, data should be analysed by multiple procedures and conclusions should be drawn based on a joint assessment, not unlike peer review processes in histopathology.
代谢组学数据库的建立——十年经验回顾
从28天大鼠研究(OECD 407)的血液样本中获得的约750种化合物的代谢组谱与一个数据库中10年以上的毒性谱相结合,以预测新化合物的毒性。我们提供了详细的程序描述和“组学数据库”的建议。控制可变性(生物、取样/储存和技术测量)是必不可少的。在大型项目开始时,应广泛调查控制变异性。参比(阳性对照)物质应用于评价并获得良好的信噪比。程序应记录在标准操作程序中,并严格执行。应定期进行阳性对照的精确重复,以评估阳性反应的可变性。控制数据应定期检查班次和分析,以获得有关正常情况的信息。如果可能的话,数据应通过多种程序进行分析,结论应基于联合评估得出,这与组织病理学中的同行评议过程没有什么不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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