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.

生物信息学和数据集成核心（BDIC） 系统分析阿尔茨海默病的绒猴模型的翻译相关性将需要一个 集中的数据管理和分析团队，与所有项目和核心进行交互。为此中央 生物信息学和数据集成核心（BDIC）将：生成多组学数据来表征绒猴 基因组、蛋白质组和转录组;为测序、基因组学和 类似的分子数据;导入和管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，系统地将人类和绒猴数据进行多尺度跨物种比对 分析;并创建和填充一个数据共享平台，以广泛和开放地访问所有数据和协议。 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 来自老化远交和突变绒猴种群的分子数据;（2）开发和应用统计方法 用于基于匹配的多组学测量的严格的绒猴和跨物种数据分析;以及（3）广泛地 通过开放的科学平台传播数据和协议，以确保可靠地复制研究， 重新使用所有绒猴数据。该中心将利用现有的资源，如广告知识门户网站、 Agora平台，Alz-PED，以及来自现有的NIA资助的联盟的工作流程，包括AMP-AD，EMP-AD， MODEL-AD、MOVE-AD和弹性-AD。我们将把这个以绒猴为中心的项目的成果添加到这个 优化翻译相关性和负责任的数据重用的基础。为了实现这些目标，BDIC 将在多个项目组成部分的多个背景下管理数据并促进其综合分析。 内部和外部研究人员将能够有效和可靠地评估以下疾病的相关性： 绒猴模型和相关表型。最后，更广泛的研究社区将被授权， 利用绒猴作为模型来理解和指导他们对阿尔茨海默病治疗方法的研究。