Core D: Data Management, Biostatistics, and Bioinformatics

核心 D：数据管理、生物统计学和生物信息学

• 批准号: 10447158
• 负责人: David V Conti
• 金额: $ 62.5万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-05 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10447158
• 关键词: African American; African ancestry; Baseline Surveys; Big Data; Bioinformatics; Biometry; California; Cells; Censuses; Cloud Computing; Collaborations; Data; Data Analyses; Data Store; Database Management Systems; Databases; Development; Ensure; Environment; Evaluation; Familiarity; Foundations; Genes; Genetic; Genomics; Genotype; Genotype-Tissue Expression Project; Heterogeneity; Human Resources; Immune; Immunity; Incubators; Individual; Informatics; Joints; Link; Maintenance; Malignant Neoplasms; Malignant neoplasm of prostate; Measures; Mediation; Methodology; Neighborhoods; Ontology; Pathology; Process; Questionnaires; Research; Research Personnel; Resources; Risk Factors; Role; Science; Security; Specimen; Statistical Data Interpretation; Stress; Subgroup; Systems Analysis; The Cancer Genome Atlas; Tumor Bank; Universities; Variant; Vital Status; base; benefit sharing; bioinformatics pipeline; cloud based; cohesion; cohort; computing resources; data hub; data integration; data management; data resource; diverse data; exome sequencing; genome-wide; men; multidimensional data; multiple data sources; neoplasm registry; non-genetic; programs; prospective; prostate cancer risk; recruit; relational database; social stressor; socioeconomics; success; tool; translational genomics; tumor microenvironment

Abstract – Data Management, Biostatistics, and Bioinformatics Core D The Data Management, Biostatistics, and Bioinformatics Core will provide a local and cloud-based data management system for the entire Program Project that will enable Project-specific analyses, as well as integrated analyses utilizing data spanning the entire Project. The Core will serve as the centralized data hub for the establishment of the prospective RESPOND cohort of African American (AA) men with prostate cancer (PCa). This includes: (1) questionnaire data, including multi-level factors related to social stressors, linkages to neighborhood socioeconomic indicators that will be collected through the recruitment Core B and primarily used in Project 1; (2) germline genome-wide genotype data and exome-sequencing data generated as part of Project 2; (3) somatic and expression data generated as part of Project 3; (4) immune cell profiles assessing the tumor microenvironment in Project 4; (5) and pathology related assessments of banked tumor specimens from Core C. The Core will also provide statistical and bioinformatics support and expertise. As a cohesive unit, the Core will leverage familiarity with data resources and commonalities of analysis across Projects. Finally, the Core will serve as the foundation for performing integrated analysis. This includes sophisticated integrated statistical analyses and access of joint results across Projects. The Core’s activities will be primarily performed within the University of Southern California’s Biostatistics Division and Translational Genomics group – benefiting from shared computational resources, active collaborations, and recent methodological developments in statistical approaches, integrated analysis and informatics. The Core is essential to the overall success of the Program Project as it serves as an incubator to process and store data, advise and complete Project-specific analyses, and perform and accelerate cross-Project integration and discoveries.

摘要-数据管理，生物统计学和生物信息学核心D 数据管理、生物统计学和生物信息学核心将提供本地和基于云的数据 整个计划项目的管理系统，该系统将实现项目特定分析，以及 利用整个项目的数据进行综合分析。核心将作为集中的数据中心 建立非裔美国人（AA）前列腺癌男性的前瞻性RESPOND队列 （PCa）。这包括：（1）问卷调查数据，包括与社会压力源相关的多层次因素， 邻里社会经济指标，将通过招聘核心B和主要收集 （2）生殖系全基因组基因型数据和外显子组测序数据，作为项目1的一部分， 项目2;（3）作为项目3的一部分生成的体细胞和表达数据;（4）免疫细胞谱评估 项目4中的肿瘤微环境;（5）和库存肿瘤标本的病理学相关评估 核心C该核心还将提供统计和生物信息学方面的支持和专门知识。作为一个有凝聚力 核心将利用对数据资源的熟悉程度和跨项目分析的共性。 最后，核心将作为执行综合分析的基础。这包括复杂的 综合统计分析和访问跨项目的联合结果。核心的活动将主要是 在南加州大学生物统计学部和翻译基因组学内进行 小组-受益于共享的计算资源，积极的合作，以及最近的方法 统计方法、综合分析和信息学方面的发展。核心对整体至关重要 该计划项目的成功，因为它作为一个孵化器，处理和存储数据，建议和完成 项目特定分析，执行并加速跨项目集成和发现。