Biodata Mining Group, CeBiTec, Bielefeld University, Bielefeld, Germany, Computational Genomics, CeBiTec, Bielefeld University, Bielefeld, Germany, Bruker Daltonik GmbH, Bremen, Germany, Proteome and Metabolome Research, Bielefeld University, Bielefeld, Germany and Max Rubner-Institute, Detmold, Germany.
Bioinformatics. 2013 Oct 1;29(19):2452-9. doi: 10.1093/bioinformatics/btt414. Epub 2013 Aug 5.
The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization.
To cover and keep up with these requirements, we have created MeltDB 2.0, a next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions.
The system is publicly available at https://meltdb.cebitec.uni-bielefeld.de. A login is required but freely available.
在过去的几年中,代谢组学研究领域获得了巨大的关注和发展。由于其独特的跨学科性,它需要结合来自不同科学领域的知识。高通量技术的进步以及随之而来的数据质量和数量的提高,对应用分析和计算方法提出了新的要求。最终生成和分析的数据集的探索还依赖于强大的数据挖掘和可视化工具。
为了满足这些需求,我们创建了 MeltDB 2.0,这是一个下一代的 Web 应用程序,用于存储、共享、标准化、整合和分析代谢组学实验。新功能提高了从色谱原始数据预处理到新生物学知识推导的整个处理流程的效率和效果。首先,高质量代谢数据集的生成大大简化了。其次,新的统计工具包允许根据广泛的科学和探索性问题来研究这些数据集。
该系统可在 https://meltdb.cebitec.uni-bielefeld.de 上公开获取。需要登录,但可免费获取。