Immunogenomics and Systems Biology Core

摘要 密歇根大学的免疫基因组学和系统生物学核心（ISCB）将支持数据分析， 管理和疾病翻译，以满足整个AMP AIM网络的目标。ISCB将基于 基于我们在遗传学和系统生物学方法开发方面的丰富经验， 广泛的多组学数据，包括遗传，表观遗传，单细胞和空间序列数据，以增加我们的 了解导致疾病发病机制和异质性的因素。我们的前提是 方法是将遗传信息与通过AMP AIM网络收集的其他组学数据相结合， 将促进和加速发现关键的致病机制，并帮助解开生物过程 导致疾病的异质性。这种方法将利用先进的因果分析方法， 机制推理、建模以及单细胞和空间基因组学数据集成。为了实现这一目标，我们将（i） 部署高度定制、可扩展且强大的数据处理能力和标准化管道， 多组学和综合临床数据集的策展、处理、管理和共享; ii）构建 全面的疾病特异性路线图，并定义AMP AIM的共享和独特的分子机制 利用统计遗传学和基因组学方法靶向疾病; iii）识别、表征和改进 通过整合多组学和临床数据塑造疾病异质性和干预反应的生物标志物 通过统计和计算模型。

ABSTRACT The Immunogenomics and Systems Biology Core (ISCB) at University of Michigan will support the data analytics, management, and disease translation to meet the goals of the entire AMP AIM Network. The ISCB will be based on our extensive experience in the development of genetics and systems biology approaches, working with a broad range of multi-omics data including genetic, epigenetic, single-cell and spatial-seq data, to increase our understanding of the factors that contribute to disease pathogenesis and heterogeneity. The premise of our approach is that integrating genetic information with other -omic data, collected through the AMP AIM Network, will facilitate and accelerate discovery of critical pathogenic mechanisms and help unravel the biologic processes contributing to disease heterogeneity. This approach will utilize advanced analytical approaches in causal mechanism inference, modeling and single cell and spatial genomics data integration. To achieve this we will i) deploy highly customized, scalable, and robust data processing capabilities and standardized pipelines for curation, processing, management, and sharing of multi-omics and integrated clinical datasets; ii) build comprehensive disease specific roadmaps, and define shared and unique molecular mechanisms of AMP AIM target diseases utilizing statistical genetics and genomics approaches; iii) identify, characterize, and refine biomarkers shaping disease heterogeneity and intervention response by integrating multi-omics and clinical data through statistical and computational modeling.