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Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium

机译:翻译研究中的数据和知识管理:IMI Oncotrack财团的Etriks平台的实施

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For large international research consortia, such as those funded by the European Union's Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers. In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.
机译:对于大型国际研究的联盟,例如由欧盟地平线2020计划或创新的药品计划资助的,良好的数据协调实践和工具对于成功的收集,组织和分析结果,良好的数据协调实践和工具至关重要。研究联盟正在通过利用全基因组测序,蛋白质组学,患者衍生的生物模型和基于计算机的系统生物学模拟等技术来尝试更雄心勃勃的科学来更好地了解疾病。 IMI etriks联盟被指控开发一个能够支持这些研究计划生成的数据复杂性的综合知识管理平台的任务。在本文中,使用Oncotrack联盟的示例,我们描述了翻译医学中的典型用例。实现了传输知识管理平台,以支持来自观察临床群组的数据,来自小鼠异种移植肿瘤模型的细胞培养模型和药物反应数据的药物反应数据。来自相应生物材料的分子分析的高尺寸(OMIC)数据与这些收集有关,使得用户可以浏览和分析这些以获得候选生物标志物。在所有这些步骤中,通过传输集成平台自动处理数据映射,链接和准备。因此,没有专业数据处理技能的研究人员可以直接关注科学问题,而无需在处理数据和数据集成的情况下花费不必要的努力,否则是一种负担和最耗时的转化研究数据分析。

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