Harmonising and Unifying Blood Metabolomic Analysis Networks (HUMAN)

代谢组学提供了被检查样本的代谢状态的实时视图。在过去的十年中，该领域显示出强劲的增长，但该领域固有的局限性阻碍了该领域在流行病学水平上的进一步应用。主要障碍包括：分析物分子结构多样，标记识别缓慢，浓度差异大，验证能力差，来自不同分析的数据组合不完整，研究支离破碎。该联盟将来自不同互补学科和部门的科学家聚集在一起，合作并建立一个研究培训网络，将基础设施经验、知识和技能结合在一起。研究范围是找出阻碍发展的问题的根源，并提出克服这些问题的措施。通过研究培训将提拔新一代组学研究人员。建立网络，通过借调联合起来，将提高研究效率，促进知识的转移。该项目将对10名ESRS进行工作包培训，旨在改进实验设计、协调分析方法、改进数据挖掘和生化途径分析以及翻译研究。我们的目标是研究样品的稳定性和制备(包括血液和替代形式，如干血斑)，生物标志物识别，多个数据集的开发，促进标准程序，开发健壮的管道，开发和实现机器可搜索的元数据标记法，用于数据存储的中央数据库，比较数据集，自动化跨实验室数据组合，开发多维数据挖掘的新算法和重建生化途径。总体目标是对ESRS进行尖端代谢组学研究方面的培训，同时通过以患者为中心的抽样提供代谢组学民主化的概念证明。

Metabolomics provides a real-time view of the metabolic state of the examined samples. The past decade the field showed strong growth, however limitations intrinsic to the field hinder further application in epidemiology level. Key obstacles include: variety of analyte molecular structures, slow marker identification, large differences in concentrations, poor validation, incomplete combination of data from different analyses and fragmentation of research. The consortium brings together scientists from different complementary disciplines and sectors to collaborate and set a research training network, combining infrastructure experience, knowledge and skills. The research scope is to identify the source of problems that hinder development, and recommend measures to overcome these. Training through research will promote a new generation of omics researchers. Networking, joining forces via secondments will enhance researchproductivity transfer of knowledge. The project will train 10 ESRs in work-packages aiming toward improvement of design of experiment, harmonization of analytical methods, improved Data Mining and biochemical pathway analysis and translational research. Application will be in the study of blood metabolome of exhaustive physical exercise.We aim to study sample stability & preparation (including blood and alternative forms such as dried blood spots), biomarker identification, exploitation of multiple datasets, promote standard procedures, develop robust pipelines, develop and implement machine searchable notations of metadata, central database for data storage, compare datasets, automate cross-laboratory data combination, develop novel algorithms for multidimensional data mining and reconstruct biochemical pathways. The overall goal is to train the ESRs in cutting edge metabolomics research and at the same time provide proof of concept of democratizing metabolomics by the use of patient centric sampling.