Semantic Integration of ImmPort and the Linked Data Cloud for Systems Vaccinology

项目总结 流感是一种高影响力的呼吸道病原体，在全球范围内影响人类健康。流感与 严重的发病率和死亡率，美国每年有30,000至40,000人死亡。疫苗接种仍在继续 预防流感的主要方法。然而，尽管一年一度的公共卫生总体取得了成功 在接种流感疫苗时，许多人未能引起显著的抗体反应。受损疫苗 在老年人中，反应是一个特别的问题，估计疗效约为50%或更差。改进 了解影响疫苗免疫反应的生物学机制可能会提供线索。 关于新的候选疫苗和疫苗接种策略。系统疫苗学研究结合了高通量 带有计算分析的实验分析技术，提供了疫苗的综合、动态视图- 驱动免疫反应。这些研究的有效性通过整合多项研究而得到加强，例如 单个研究的队列规模通常很小，结果因人群的差异而有所不同 程序，以及其他批处理效果。流感疫苗接种研究占非临床试验的近20% 目前在NIH/NIAID ImmPort库中可用的研究，并为二次分析提供了潜在的 确定疫苗接种反应的强健特征，以指导疫苗效力的提高。然而， 这种集成很耗时、容易出错，并且需要技术编程专业知识，而不是 许多研究人员团体都可以访问。语义网提供了一个理论和技术框架 通过它，可以通过利用本体映射来链接相关数据和 实现公平的数据原则(可查找性、可访问性、互操作性和可重用性)。在此，我们建议 利用语义Web框架将ImmPort疫苗接种研究相互链接，并链接到无数 对系统疫苗学至关重要的外部公共资源(例如，途径数据库、基因本体论 注释和出版物)。由此产生的系统(LinkedImm)将包括用于假设的公共接口- 基于查询，并将应用于多项研究分析，以确定流感的稳健时间特征 疫苗接种反应。我们建议通过两个目标来实现这一目标：(目标1)利用语义网 在ImmPort整合流感疫苗研究并将其与公共资源联系起来的技术，以使 假设驱动的查询。(目标2)通过多项研究确定人类流感疫苗接种反应的特征 研究分析。总而言之，我们建议结合技术驱动的资源创造来促进 ImmPort数据再利用(目标1)和一个具有科学和医学重要性的推动生物项目(目标2)。

PROJECT SUMMARY Influenza is a high impact respiratory pathogen that affects human health worldwide. Influenza is associated with significant morbidity and mortality, with 30,000 to 40,000 annual deaths in the United States. Vaccination remains the primary method of influenza prevention. However, despite the overall public health success of annual influenza vaccinations, many individuals fail to induce a significant antibody response. Impaired vaccine responses are a particular issue in older adults, with estimates of efficacy around 50% and worse. Improved understanding of the biological mechanisms that influence the immune response to vaccination may offer clues on novel vaccine candidates and vaccination strategies. Systems vaccinology studies combine high-throughput experimental profiling techniques with computational analysis to provide an integrated, dynamic view of vaccine- driven immune responses. The validity of such studies is strengthened by integration of multiple studies, as single study cohort sizes are generally small and results vary due to differences in populations, experimental procedures, and other batch effects. Influenza vaccination studies comprise nearly 20% of the non-clinical trial studies currently available in the NIH/NIAID ImmPort repository and offer the potential for secondary analysis to identify robust signatures of vaccination responses that can guide improvements to vaccine efficacy. However, such integration is time-consuming, error-prone, and requires technical programming expertise that is not accessible to many researcher groups. The semantic web provides a theoretical and technical framework through which these problems can be addressed by leveraging ontology mappings to link related data and achieve FAIR data principles (Findability, Accessibility, Interoperability and Reusability). Here we propose to leverage the semantic web framework to link ImmPort vaccination studies to each other, and to the myriad of external public resources so critical for systems vaccinology (e.g., pathway databases, gene ontology annotations and publications). The resulting system (LinkedImm) will include public interfaces for hypothesis- based queries, and will be applied in a multi-study analysis to identify robust temporal signatures of the influenza vaccination response. We propose to achieve this goal through two aims: (Aim 1) Leverage semantic web technologies to integrate influenza vaccination studies in ImmPort and link them with public resources to allow hypothesis-driven queries. (Aim 2) Identify signatures of human influenza vaccination responses through a multi- study analysis. In summary, we propose a combination of technology-driven resource creation to facilitate ImmPort data reuse (Aim 1) with a driving biological project (Aim 2) of scientific and medical importance.