Immunogenomics and Systems Biology Core

免疫基因组学和系统生物学核心

• 批准号: 10594514
• 负责人: Johann Eli Gudjonsson
• 金额: $ 54.34万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-18 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594514
• 关键词: ATAC-seq; Acceleration; Address; Antigen-Antibody Complex; Atopic Dermatitis; Autoimmune Diseases; Autoimmune Process; Basic Science; Binding; Biological Assay; Biological Markers; Biological Process; Biology; CD8B1 gene; Cells; Chromosome Mapping; Clinical Data; Clinical Investigator; Computer Models; Computer software; Computing Methodologies; Consultations; Data; Data Analyses; Data Analytics; Data Set; Development; Disease; E-learning; Epigenetic Process; Fibroblasts; Functional disorder; Funding; Generations; Genetic; Genetic Variation; Genomic approach; Genomics; Goals; Heterogeneity; Immune; Immune System Diseases; Immunogenomics; Inflammatory; Intervention; Link; Lupus; Maps; Mediating; Michigan; Modality; Modeling; Molecular; Molecular Disease; Molecular Profiling; Multiomic Data; Pathogenesis; Pathogenicity; Performance; Population; Process; Psoriasis; Psoriatic Arthritis; Research; Research Personnel; Resources; Role; Scleroderma; Shapes; Signal Transduction; Skin; Standardization; Statistical Models; System; Systemic Lupus Erythematosus; Systems Biology; Technology; Therapeutic Intervention; Training; Translations; Universities; advanced analytics; biomarker identification; cell type; computerized data processing; cytokine; data analysis pipeline; data curation; data integration; data management; data standards; disease heterogeneity; epigenomics; experience; flexibility; genetic approach; genetic information; genomic data; high throughput screening; innovation; insight; keratinocyte; multiple data types; multiple omics; novel; open source tool; programs; response; sample collection; structured data; transcription factor; transcriptome sequencing; transcriptomics; translational scientist; treatment response; ATAC-seq; Acceleration; Address; Antigen-Antibody Complex; Atopic Dermatitis; Autoimmune Diseases; Autoimmune Process; Basic Science; Binding; Biological Assay; Biological Markers; Biological Process; Biology; CD8B1 gene; Cells; Chromosome Mapping; Clinical Data; Clinical Investigator; Computer Models; Computer software; Computing Methodologies; Consultations; Data; Data Analyses; Data Analytics; Data Set; Development; Disease; E-learning; Epigenetic Process; Fibroblasts; Functional disorder; Funding; Generations; Genetic; Genetic Variation; Genomic approach; Genomics; Goals; Heterogeneity; Immune; Immune System Diseases; Immunogenomics; Inflammatory; Intervention; Link; Lupus; Maps; Mediating; Michigan; Modality; Modeling; Molecular; Molecular Disease; Molecular Profiling; Multiomic Data; Pathogenesis; Pathogenicity; Performance; Population; Process; Psoriasis; Psoriatic Arthritis; Research; Research Personnel; Resources; Role; Scleroderma; Shapes; Signal Transduction; Skin; Standardization; Statistical Models; System; Systemic Lupus Erythematosus; Systems Biology; Technology; Therapeutic Intervention; Training; Translations; Universities; advanced analytics; biomarker identification; cell type; computerized data processing; cytokine; data analysis pipeline; data curation; data integration; data management; data standards; disease heterogeneity; epigenomics; experience; flexibility; genetic approach; genetic information; genomic data; high throughput screening; innovation; insight; keratinocyte; multiple data types; multiple omics; novel; open source tool; programs; response; sample collection; structured data; transcription factor; transcriptome sequencing; transcriptomics; translational scientist; treatment response

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.

抽象的 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 关于我们在遗传学和系统生物学方法发展的丰富经验，与 多种多样的数据包括遗传，表观遗传学，单细胞和空间式数据，以增加我们的 了解导致疾病发病机理和异质性的因素。我们的前提 方法是通过AMP AIM网络收集的其他遗传信息与其他遗传信息集成在一起， 将促进和加速发现关键的致病机制，并有助于揭示生物学过程 导致疾病异质性。这种方法将利用因果的高级分析方法 机理推断，建模以及单细胞和空间基因组学数据整合。为了实现这一目标，我将 部署高度定制，可扩展和健壮的数据处理功能以及标准化管道 策划，加工，管理和共享多词和综合临床数据集； ii）构建 综合疾病特定的路线图，并定义了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的目标疾病； iii）确定，特征和完善 生物标志物通过整合多词和临床数据来塑造疾病异质性和干预反应 通过统计和计算建模。