Technology and Computational Core

项目摘要核心B 该计划项目的目标是将一个多学科团队聚集在一起，以产生必要的信息 用于设计和测试下一代冠状病毒疫苗战略，将有最大可能 覆盖其他CoV的范围。所有三个项目的结果都将为泛冠状病毒疫苗的设计提供参考 对抗不断演变的SARS-CoV-2变种和其他冠状病毒，以遏制当前和防止未来的大流行。 这将通过三个动态和综合的项目来实现，该项目审查以下各主要方面 疫苗策略。技术和计算核心B将通过与每个 其他研究人员，所有三个项目都是为了从拟议的实验工作中获得最大的洞察力。基于 需要集中的组织和血液处理、单细胞基因组学和TCR测序数据生成， 和综合计算分析，我们假设有一个中心技术和计算 核心，而不是让每个项目独立工作，将是工作成功的关键 在本计划应用程序中提出，并将最大限度地比较和集成跨项目的数据。一个 由免疫学家、测序专家和计算生物学家组成的集中工作组是最好的方法 以确保这项研究将在最大限度地识别和使用适当的 计算方法。通过目标1，核心B将通过提供专家支持所有三个研究项目 关于执行单细胞基因组学和TCR测序实验和分析的建议和帮助 从人体采集血液和组织标本，从免疫小鼠采集肺标本的策略。这个 核心工作人员提供的标准化框架将通过以下方式增加所有实验的严格性和可重复性 消除任何可能出现的变异。核心B还将在多变量 免疫系统反应和抗原/抗体结合的建模(目标2)。因为……的复杂性 对病原体的免疫反应，我们从实验数据集中获得洞察力和原理的能力 在所有三个项目中生成的数据将通过综合计算分析和建模得到增强 拥抱综合系统的观点。这种复杂性源于各种问题，包括：(A) 来自多个变量的共同贡献共同支配观察到的反应，而不是任何单一的 变量本身是决定性的；(B)这些多变量贡献通常不是独立的，但 相反，这些贡献通常是相互关联或反相关的；以及(C)这些贡献往往是非线性的。多数 标准的统计技术通常会违反这些问题中的一个或多个，因此本核心的目的是 应用来自工程和计算机科学的计算方法，包括“机器学习” 技术，实际上可以容纳其中的任何一个或全部。最后，核心B将提供额外的帮助 用于任何复杂的实验设计或分析(如果需要)。帮助提高透明度、所有数据和分析 支持所有调查结果的框架将在接受出版后向公众公布。

PROJECT SUMMARY CORE B The goal of this Program Project is to bring together a multi-disciplinary team to produce information necessary for the design and testing of the next-generation of CoV vaccine strategies that will have the greatest possible breadth across other CoVs. Results from all three projects will inform design of Pan-Coronavirus vaccines against evolving SARS-CoV-2 variants and other coronaviruses to stem current and prevent future pandemics. This will be accomplished through three dynamic and integrative projects examining various key aspects of vaccine strategies. The Technology and Computational Core B will support, in close interaction with each of the other investigators, all three Projects to gain maximal insight from the proposed experimental work. Based on the need for centralized tissue and blood processing, single-cell genomics and TCR sequencing data generation, and integrative computational analyses, we hypothesized that having a central Technology and Computational Core, as opposed to having each project working independently, will be critical to the success of the work proposed in this Program application and will maximize comparisons and integration of data across projects. A centralized working group of immunologists, sequencing experts, and computational biologists is the best way to ensure that this research will be properly carried out with maximal identification and use of appropriate computational methods. Through Aim 1, Core B will support all three Research Projects by providing expert advice and assistance on executing single-cell genomics and TCR sequencing experimental and analytical strategies of blood and tissue specimens collected from human subjects and lung from immunized mice. The standardized frameworks provided by the Core staff will add rigor and reproducibility to all experiments by removing any variation that might otherwise arise. Core B will also provide collaborative efforts on multi-variate modeling of immune system response and antigen/antibody binding (Aim 2). Because of the complexity of immune responses to pathogens, our ability to gain insights and principles from the experimental datasets generated across all three Projects will be enhanced by integrative computational analysis and modeling embracing an integrative systems perspective. This complexity derives from diverse issues including: (a) concomitant contributions from multiple variables together govern observed responses, rather than any single variable being determinative by itself; (b) these multi-variate contributions are generally not independent, but instead are typically co- or anti-correlated; and (c) these contributions often are non-linear in nature. Most standard statistical techniques typically violate one or more of these issues, so the purpose of this Core is to apply computational approaches arising from engineering and computer science, including “machine learning” techniques, that can in fact accommodate any or all of them. Finally, Core B will provide additional assistance for any complicated experimental design or analysis if/as needed. To aid with transparency, all data and analysis frameworks supporting all findings will be made publicly available upon acceptance for publication.