Immunogenomics and Systems Biology Core

免疫基因组学和系统生物学核心

• 批准号: 10452138
• 负责人: Johann Eli Gudjonsson
• 金额: $ 30万
• 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/10452138
• 关键词: ATAC-seq; 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; Clinical Data; Clinical Investigator; Computer Models; Computer software; Computing Methodologies; Consultations; Custom; Data; Data Analyses; Data Analytics; Data Set; Development; Disease; Epigenetic Process; Fibroblasts; Functional disorder; Funding; Generations; Genetic; Genetic Variation; Genomic approach; Genomics; Goals; Heterogeneity; Immune; Immune System Diseases; Immunogenomics; Inflammatory; Institutes; 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; Shapes; Signal Transduction; Skin; Standardization; Statistical Models; System; Systemic Lupus Erythematosus; Systems Biology; Technology; Therapeutic Intervention; Training; Translations; Universities; advanced analytics; base; 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; ranpirnase; 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.

摘要