Bioinformatics Core

生物信息学核心

• 批准号: 10544810
• 负责人: Kouros Owzar
• 金额: $ 31.68万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10544810
• 关键词: Accounting; Address; Adherence; Applications Grants; Bioinformatics; Bioinformatics Shared Resource; Biometry; Biostatistics Shared Resource; Cancer Center; Cancer Center Support Grant; ChIP-seq; Charge; Clinical; Clinical Research; Cloud Computing; Cloud Service; Collaborations; Complex; Computational Biology; Contracts; DNA sequencing; Data; Data Provenance; Data Set; Data Storage and Retrieval; Databases; Development; Faculty; Funding; Genetic; Genomics; Grant; Health; High-Throughput Nucleotide Sequencing; Image; Informatics; Information Systems; Infrastructure; Institute of Medicine (U.S.); Journals; Machine Learning; Malignant Neoplasms; Manuscripts; Methodology; Methods; Mission; Modeling; Pathology; Pediatric Oncology; Phenotype; Philosophy; Publications; Publishing; Quality Control; Reproducibility; Research; Research Institute; Research Personnel; Research Project Grants; Research Support; Resource Sharing; Resources; Running; Science; Services; Source Code; Standardization; Statistical Data Interpretation; T cell receptor repertoire sequencing; The Cancer Genome Atlas; Writing; admixture mapping; analysis pipeline; biobank; bioinformatics infrastructure; cancer genomics; cell free DNA; computerized tools; cost; cost effectiveness; cost efficient; data integration; data management; data registry; database of Genotypes and Phenotypes; design; diverse data; empowerment; functional genomics; genome sequencing; genome wide association study; genomic data; large datasets; literate; medical schools; member; microbiome; multidimensional data; open source; programs; sequencing platform; single cell analysis; single-cell RNA sequencing; sound; tool; trait; translational medicine; tumor; whole genome

ABSTRACT – BIOINFORMATICS SHARED RESOURCE The Bioinformatics (BIn) shared resource, a core function of the Duke Cancer Institute (DCI), serves as a centralized resource for expertise in applied and theoretical cancer bioinformatics, genomics, computational biology, machine learning and statistical genetics. The faculty and staff members of this shared resource support DCI members across the continuum of research, including experimental and statistical design for genomic studies, complex genomic data management, integration of diverse data sets, computing and statistical analysis, and machine learning. The shared resource provides support for investigator-generated data as well as retrospective data from research databases (e.g., GDC [large datasets like TCGA] or dbGAP). The resource’s mission is to provide high-quality, service-oriented, coordinated and cost-efficient bioinformatics support and infrastructure for DCI members. Emphasis is placed on facilitating increased collaborations across DCI programs and other DCI shared resources. The mission of this shared resource is addressed within the framework of adherence to 3 principles: 1- sound data provenance and statistical principles, 2- literate programming, and 3- reproducible analysis. The BIn shared resource provides and runs standardized genomic analysis pipelines (e.g., germline, tumor and cell-free DNA-Seq, bulk and single-cell RNA-Seq, CHiP-Seq, and sequencing of T-Cell Receptor repertoire and microbiome). These pipelines are constructed on the basis of state-of-the-art published tools, maintained under strict source code version control and designed to be extensible and deployable in a scalable fashion on local servers and cloud services. The philosophy of the BIn shared resource is that the scope of scientific discovery and rigor should neither be limited nor compromised due to lack of appropriate and adequate statistical methodology or computational tools. When needed and appropriate, the faculty and staff of the shared resource extend or revise existing or develop de novo methods and computational tools to enable DCI members to address scientific questions with requisite rigor and efficiency. In addition to computing and analysis support, the shared resource provides extensive support for writing of scientific abstracts and manuscripts, as well as grant proposals. The BIn shared resource also serves as a liaison and facilitator between DCI members and other DCI shared resources (e.g., the Biostatistics, Functional Genomics, Information Systems, and BioRepository and Precision Pathology Center Shared Resources). The research support, services and resources of the BIn shared resource are provided exclusively to DCI members. In 2018, the BIn shared resource provided services to 53 investigators, 100% of whom were DCI members, accounting for 100% of total usage, from all 8 DCI Research Programs. Use of this shared resource by DCI members contributed to 178 publications over the project period, 41 of which were in high impact journals, demonstrating the value of services offered by the resource.

摘要 - 生物信息学共享资源 生物信息学（BIN）共享资源是杜克癌症研究所（DCI）的核心功能，作为一个 用于应用和理论癌症癌症生物信息学，基因组学，计算的集中资源 生物学，机器学习和统计遗传学。 在整个研究的连续体中，DCI成员包括基因组的实验和统计设计 研究，复杂的基因组数据管理，各种数据集的集成，计算和统计分析， 和机器学习。共享资源为研究者生成的数据提供了支持 来自研究数据库的回顾性数据（例如，GDC [诸如TCGA之类的大数据集]或DBGAP） 使命是提供高质量，面向服务，协调和成本效益的生物信息学支持和 DCI成员的基础架构。 其他DCI共享资源。 遵守3个原则：1-声音数据pronational和统计原理，2级编程和3-- 可重复的分析。 种系，肿瘤和无细胞的DNA-seq，Bulk和单细胞RNA-Seq，ChIP-Seq以及T细胞的测序 受体曲目和微生物组）。 在严格的源代码代码控制下维护的工具，设计为可扩展和可部署 本地服务器和云服务上的可扩展方式。 垃圾箱共享资源的理念是，科学发现和严格不应 由于缺乏适当且足够的统计方法或计算而受到限制或妥协 工具。 开发从头方法和计算工具，以使DCI成员能够通过 必要的严格和效率。 对撰写科学摘要和手稿的广泛支持，以及赠款 资源还可以作为DCI成员与其他DCI共享资源之间的联络和促进者（例如 生物统计学，功能基因组学，信息系统以及生物疾病和精度病理中心 共享资源）。 专门针对DCI成员。 2018年，垃圾箱共享资源为53名调查人员提供了服务，其中100％是DCI成员， 从所有8个DCI研究计划中占总使用的100％。 成员在项目期间为178个出版物做出了贡献，其中41个是在高影响期刊中， 证明资源提供的服务价值。