Data analytics and bioinformatics Core

数据分析和生物信息学核心

• 批准号: 10395999
• 负责人: Alexander Rosenberg
• 金额: $ 44.45万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10395999
• 关键词: Address; Antigens; Area; Atlases; B-Cell Antigen Receptor; B-Lymphocyte Subsets; B-Lymphocytes; B-cell receptor repertoire sequencing; Bioinformatics; Collaborations; Computer software; Consumption; Custom; Data; Data Analyses; Data Analytics; Data Commons; Data Set; Databases; Dideoxy Chain Termination DNA Sequencing; Ensure; Environment; Eye; FAIR principles; Flow Cytometry; Future; Goals; Hand; Human; Human Resources; Immunity; Immunologics; Immunology procedure; Individual; Informatics; Infrastructure; Institution; Leadership; Libraries; Lymphoid; Memory B-Lymphocyte; Metadata; Methodology; Methods; Molecular; Mucous Membrane; Nature; Organ; Peripheral; Phenotype; Plasma Cells; Play; Procedures; Process; Reporting; Reproducibility; Research; Research Design; Research Personnel; Resources; Role; Sampling; Sequence Analysis; Services; Software Framework; Standardization; System; Talents; Testing; Tissues; Visualization; Writing; complex data; computerized data processing; data access; data analysis pipeline; data infrastructure; data integrity; data management; data quality; data sharing; data standards; design; experience; information processing; member; next generation sequencing; programs; repository; single cell analysis; single-cell RNA sequencing; statistics; synergism; technology development; tissue processing; transcriptome; transcriptome sequencing; transcriptomics

Core D: Data Analytics and Bioinformatics Core Project Summary Hypothesis-testing studies designed to understand the phenotypic, molecular and functional differences between antigen-specific B cell subsets found in human lymphoid and peripheral mucosal tissues that are described in this proposal will rely on several ‘omics and other data-rich approaches and corresponding analyses to achieve their goals. To support use of these platforms and their associated data analyses, we propose to provide unified and integrated data and informatics services in Core D, the Data Analytics and Bioinformatics Core. The core will cover three broad areas as reflected by the Specific Aims: data management, data processing pipelines, and collaborative, downstream analysis. In Specific Aim 1, we propose to oversee and implement systems and processes to manage donor demographic and sample processing metadata, resulting data sets (raw through processed) and analysis provenance with all the appropriate linkages. This will benefit the Program by creating infrastructure that can be efficiently used by all the Projects and Cores as well as promoting good data stewardship practices which in turn, supports reproducibility. In Specific Aim 2, we propose to implement standardized workflows to cover all of the high-throughput platforms used in this Program (by one or more Projects): RNA-seq, single-cell RNA-seq, B cell receptor sequencing (for Sanger sequencing as well as for repertoires, by next-generation sequencing), and multi-parameter flow cytometry using semi-automated approaches. This will benefit the Program by standardizing primary data processing across the Projects and will promote comparability of results. In Specific Aim 3, we will provide collaborative downstream bioinformatics analytical and statistical support for all three Projects. This will benefit the Program by serving as a resource that all Investigators in the Projects can access for using data analyses to address their hypotheses, and by centralizing this function, we will economize this support as the analytical needs and methodologies of the Projects will overlap. Importantly, this will also enable creation of a molecular atlas of memory B cells and plasma cells across tissues, integrating transcriptomic, phenotypic and B cell receptor repertoire data from the Projects - we will assemble such a data set with an eye to future incorporation into a Data Commons environment. To develop this Core, we have assembled a strong team with experienced leadership and talented individuals with demonstrated expertise in all of the areas covered. Combining these three broad areas into a Core will maximize efficiency, standardize process and promote scientific synergy across the Program.

核心D：数据分析和生物信息学核心 项目摘要 假设检验研究，旨在了解表型，分子和功能差异之间 在人淋巴和外周粘膜组织中发现的抗原特异性B细胞亚群， 这项建议将依赖于几个“组学”和其他数据丰富的方法和相应的分析，以实现 他们的目标为了支持这些平台的使用及其相关的数据分析，我们建议提供统一的 以及核心D中的集成数据和信息学服务，即数据分析和生物信息学核心。的 核心将涵盖具体目标所反映的三大领域：数据管理，数据处理管道， 和协作的下游分析。在具体目标1中，我们建议监督和实施系统， 管理供体人口统计和样本处理元数据的流程， 处理）和分析出处与所有适当的联系。这将有利于该计划， 所有项目和核心都可以有效使用的基础设施，并促进良好的数据 这反过来又支持可重复性。在具体目标2中，我们建议实施 标准化的工作流程，以涵盖本计划中使用的所有高通量平台（由一个或多个 项目）：RNA-seq、单细胞RNA-seq、B细胞受体测序（用于桑格测序以及用于 库，通过下一代测序），和多参数流式细胞术，使用半自动 接近。这将通过标准化项目间的主要数据处理使计划受益， 促进结果的可比性。在具体目标3中，我们将提供合作的下游生物信息学 为所有三个项目提供分析和统计支助。这将有利于该计划作为一种资源， 项目中的所有研究者都可以使用数据分析来解决他们的假设， 通过集中这一职能，我们将节省这一支助，因为分析需要和方法 项目将重叠。重要的是，这也将使得能够创建记忆B细胞和血浆的分子图谱 整合来自项目的转录组、表型和B细胞受体库数据， - 我们将汇集这样一个数据集，着眼于未来纳入数据共享环境。到 为了发展这一核心，我们组建了一支强大的团队，拥有经验丰富的领导层和才华横溢的个人， 在所涉及的所有领域展示了专业知识。将这三大领域结合成一个核心， 提高效率，规范流程，促进整个计划的科学协同。