Bioinformatics and Data Integration Core

生物信息学和数据集成核心

• 批准号: 10494771
• 负责人: Gregory W Carter
• 金额: $ 110.68万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10494771
• 关键词: Address; Aging; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease therapeutic; Bioinformatics; Callithrix; Cohort Studies; Communities; Data; Data Analyses; Data Storage and Retrieval; Disease; Ensure; FAIR principles; Foundations; Funding; Generations; Genome; Genomics; Goals; Human; Human Genetics; Human Resources; Image; Knowledge Portal; Link; Maps; Measures; Modeling; Molecular; Molecular Profiling; Multiomic Data; Outcome; Phenotype; Population; Positioning Attribute; Process; Proteome; Proteomics; Protocols documentation; Quality Control; Research; Research Personnel; Resources; Software Tools; Standardization; Statistical Data Interpretation; Statistical Methods; Validation; base; behavioral outcome; computerized data processing; data format; data integration; data management; data reuse; design; empowered; genome sequencing; genome-wide; genomic data; human data; molecular pathology; molecular scale; multiple omics; multiscale data; mutant; open data; resilience; sharing platform; therapeutic development; tool; transcriptome; transcriptome sequencing; uptake; whole genome

PROJECT SUMMARY BIOINFORMATICS AND DATA INTEGRATION CORE (BDIC) Systematic analysis of the translational relevance of marmoset models of Alzheimer’s disease will require a centralized data management and analysis team that interacts with all Projects and Cores. To this end, the Bioinformatics and Data Integration Core (BDIC) will: generate multi-omics data to characterize marmoset genomes, proteomes, and transcriptomes; provide data processing and storage for sequencing, genomics, and similar molecular data; import and curate data and results from relevant human genetic and genomic studies; create the processes and tools to systematically align human and marmoset data for multi-scale cross-species analyses; and create and populate a data-sharing platform for broad and open access of all data and protocols. This core will address this goal through the following aims: (1) generate, process, and manage genome-scale molecular data from aging outbred and mutant marmoset populations; (2) develop and apply statistical methods for rigorous marmoset and cross-species data analysis based on matched multi-omic measures; and (3) broadly disseminate data and protocols through open science platforms to ensure reliable replication of studies and reuse of all marmoset data. The BDIC will build upon existing resources such as the AD Knowledge Portal, the Agora platform, Alz-PED, and the workflows from existing NIA-funded consortia, including AMP-AD, TREAT-AD, MODEL-AD, MOVE-AD, and Resilience-AD. We will add the outcomes of this marmoset-centered project to this foundation to optimize translational relevance and responsible data reuse. In achieving these goals, the BDIC will manage data and facilitate its integrated analysis in multiple contexts across multiple project components. Internal and external researchers will be positioned to efficiently and reliably assess the disease relevance of marmoset models and associated phenotypes. Finally, the broader research community will be empowered to use the marmoset as a model to understand and guide their research into therapeutics for Alzheimer’s disease.

项目摘要生物信息学和数据集成核心 (BDIC) 对阿尔茨海默病狨猴模型的转化相关性进行系统分析需要 为此，与所有项目和核心进行交互的集中数据管理和分析团队。 生物信息学和数据集成核心 (BDIC) 将： 生成多组学数据来表征狨猴 基因组、蛋白质组和转录组；为测序、基因组学和分析提供数据处理和存储； 类似的分子数据；导入和整理相关人类遗传和基因组研究的数据和结果； 创建流程和工具来系统地调整人类和狨猴数据以实现多尺度跨物种 分析；创建和填充数据共享平台，以实现所有数据和协议的广泛和开放访问。 该核心将通过以下目标来实现这一目标：(1) 生成、处理和管理基因组规模 来自衰老远交和突变狨猴种群的分子数据（2）开发和应用统计方法； 基于匹配的多组学测量进行严格的狨猴和跨物种数据分析；以及（3）广泛 通过开放科学平台传播数据和协议，以确保研究的可靠复制和 BDIC 将利用现有资源，例如 AD 知识门户、 Agora 平台、Alz-PED 以及 NIA 资助的现有联盟的工作流程，包括 AMP-AD、TREAT-AD、 我们将以狨猴为中心的项目的成果添加到 MODEL-AD、MOVE-AD 和 Resilience-AD 中。 BDIC 为优化翻译相关性和负责任的数据重用奠定了基础。 将管理数据并促进跨多个项目组件的多个上下文中的集成分析。 内部和外部研究人员将能够有效、可靠地评估以下疾病的相关性： 最后，更广泛的研究界将有能力开发狨猴模型和相关表型。 使用狨猴作为模型来理解和指导他们对阿尔茨海默病治疗的研究。