Harmonising and Unifying Blood Metabolomic Analysis Networks (HUMAN)

代谢组学提供了检测样品代谢状态的实时视图。近十年来，该领域取得了长足的发展，但其固有的局限性阻碍了该领域在流行病学层面的进一步应用。主要障碍包括：分析物分子结构的多样性、标记物识别速度慢、浓度差异大、验证性差、不同分析数据的不完整组合以及研究的碎片化。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，将基础设施的经验、知识和技能结合起来。研究范围是确定阻碍发展的问题的根源，并建议克服这些问题的措施。通过研究培训将促进新一代组学研究人员。通过借调建立网络和联合力量将提高研究生产力和知识转移。该项目将培训10名ESRs的工作包，旨在改进实验设计、统一分析方法、改进数据挖掘和生化途径分析以及转化研究。将应用于穷尽性体育锻炼的血液代谢组学研究。我们的目标是研究样品的稳定性和制备（包括血液和其他形式，如干血斑），生物标志物鉴定，多数据集的利用，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符号，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。总体目标是培训esr进行尖端代谢组学研究，同时通过使用以患者为中心的采样，为代谢组学民主化的概念提供证据。

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.