Immune Responses to Malaria, HIV and SARS-CoV-2 Infection and Immunization- Data Management and Analysis Core

摘要 免疫学研究的大数据管理系统和计算分析方法必须 能够处理高通量、多参数分析技术，这些技术提供独特的数据管理和 分析挑战。在我们过去在人类免疫学项目联盟(HIPC)工作的基础上， 包括免疫空间重债穷国门户网站和数据资源，这项提案旨在建立一个 将简化数据分析和集成的基础设施-跨研究中心和跨分析 类型-跨越该提案的不同科学项目，从而帮助实现更有意义的生物学 对他们激励性问题的洞察。我们的数据管理和分析核心(DMAC)将提供数据 以三种方式支持本提案的科学项目所需的管理和相关服务。 首先，我们将开发用于导入、注释、预处理和标准化免疫学的管道 化验数据，以实现数据分析和沉积到我们的数据管理系统。这些管道将 在适当的情况下使用现有工具；在下列情况下，将修改工具和/或开发新工具 需要的。此外，我们将努力使用协作性的、开源的工具。第二，利用这些管道，我们 将开发一个中央数据管理系统，用于数据收集、存储和共享。该系统将是 围绕我们开发的DataPackageR R包构建，并将作为 将步骤与统计分析分开。第三，我们将提供研究设计和统计/计算支持 针对本提案的不同研究项目。这些努力对于确保高水平的 检测疫苗诱导的免疫反应并对其进行量化的灵敏度和特异度 响应具有最高的准确性、重复性和信噪比。通过实现这一目标 来自不同实验室和免疫分析技术的未经处理的原始数据将能够达到高质量 以标准化方式进行控制、注解、格式化以进行下游分析和建模，最后 存储在高度安全的共享基础设施上，提供无缝且高度可重复性的工作流 这提高了科学分析的质量。

Abstract Large-data management systems and computational analysis approaches to immunological research must be able to handle high-throughput, multiparametric assay technologies, which pose unique data management and analysis challenges. Building upon our past work in the Human Immunology Project Consortium (HIPC), including the ImmuneSpace HIPC web portal and data resource, this proposal aims to establish an infrastructure that will streamline data analysis and integration – across research centers and across assay types – across the different scientific projects of this proposal, thus helping achieve more meaningful biological insights into their motivating questions. Our Data Management and Analysis Core (DMAC) will provide data management and associated services required to support the scientific projects of this proposal in three ways. First, we will develop pipelines for importing, annotating, pre-processing, and standardizing immunological assay data to enable data analysis and for deposition into our data management system. These pipelines will utilize existing tools where appropriate; tools will be modified and/or new tools will be developed, when needed. In addition, we will strive to use collaborative, open-source tools. Second, utilizing these pipelines, we will develop a central data management system for data collection, storage, and sharing. The system will be built around the DataPackageR R package that we have developed, and will track data processing as a separate step from statistical analysis. Third, we will provide study design and statistical/computational support for the different research projects of this proposal. These efforts are particularly important for ensuring high sensitivity and specificity to detect e.g. vaccine-induced immune responses and for quantifying these responses with maximal accuracy, reproducibility, and signal-to-noise ratios. By achieving the aims of this grant, unprocessed raw data from diverse labs and immunological assay technologies will be able to be quality controlled, annotated in a standardized fashion, formatted for downstream analysis and modeling, and finally stored on a shared and highly secure infrastructure, providing a seamless and highly reproducible workflow that bolsters the quality of the scientific analyses.