Cancer Genomics:Integrative and Scalable Solutions in R / Bioconductor

癌症基因组学：R / Bioconductor 中的集成且可扩展的解决方案

• 批准号: 9122328
• 负责人: Martin T Morgan
• 金额: $ 63.29万
• 依托单位: ROSWELL PARK CANCER INSTITUTE CORP
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9122328
• 关键词: Address; Adoption; Algorithms; Bioconductor; Biological Assay; Cancer cell line; Clinical; Collection; Communities; Complex; Comprehension; Computer software; Copy Number Polymorphism; Data; Data Reporting; Data Set; Development; Environment; Evaluation; Foundations; Genes; Genome; Genomic Segment; Genomics; Health; Individual; International; Left; Link; Location; Malignant Neoplasms; Measures; Memory; Metadata; Methods; Methylation; MicroRNAs; Modeling; Pathology; Performance; Programming Languages; Publications; Research; Research Infrastructure; Research Personnel; Resources; Sampling; Slide; Software Engineering; Somatic Mutation; Specimen; Statistical Computing; Stream; Structure; Systems Biology; The Cancer Genome Atlas; Transcript; Work; anticancer research; base; cancer genome; cancer genomics; cloud based; cost; data integration; falls; flexibility; genetic information; genome annotation; genomic data; high throughput analysis; improved; meetings; microbiome; outreach; programs; repository; research study; skills; software development; task analysis; tool; whole genome

DESCRIPTION (provided by applicant): This proposal develops scalable R / Bioconductor software infrastructure and data resources to integrate complex, heterogeneous, and large cancer genomic experiments. The falling cost of genomic assays facilitates collection of multiple data types (e.g., gene and transcript expression, structural variation, copy number, methylation, and microRNA data) from a set of clinical specimens. Furthermore, substantial resources are now available from large consortium activities like The Cancer Genome Atlas (TCGA). Existing analysis pipelines focus on the treatment of a specific data type, leaving a critical need for tool for integrative analysis of multiple genomic assays for locally generated or publicly available data. R / Bioconductor has historically provided standardized genomic data structures and annotations that have enjoyed widespread adoption in the cancer genomics research community. This proposal adapts R / Bioconductor to meet the increasing conceptual and computational complexity of multi-assay cancer genomic experiments. We begin by developing software containers for coordinated representation, manipulation, and transformation of heterogeneous derived data from multiple cancer genomic assays. These containers are then extended to manage very large primary data resources. To facilitate integration of local experimental results with major public cancer genomics experiment data sets and annotations, we re-package public resources and provide software and cloud-based facilities for easy and fast programmatic access from within R/Bioconductor. This greatly simplifies cancer genomic analysis tasks that otherwise require significant, error-prone individual efforts. Finally, we provide software infrastructure to enable high-throughput computation using parallel and iterative approaches. The ability to manipulate multi-assay cancer genomic experiments, to understand individual experimental results in the context of public experiments and annotations, and facilities for improved high-throughput computational performance in a well-established computing environment greatly enhances opportunities for analysis and comprehension of large multi-assay cancer genomic experiments.

描述（由申请人提供）：该建议开发可扩展的R /生物导体软件基础架构和数据资源，以整合复杂，异质和大型癌症基因组实验。基因组测定的成本下降促进了一组临床标本的多种数据类型（例如基因和转录表达，结构变化，拷贝数，甲基化和microRNA数据）的收集。此外，现在可以从癌症基因组图集（TCGA）等大型财团活动中获得大量资源。现有的分析管道着重于处理特定数据类型的处理，这使得对本地生成或公开可用数据的多个基因组测定的综合分析具有至关重要的需求。 R / Bioconductor历来提供了标准化的基因组数据结构和注释，这些数据结构和注释在癌症基因组学研究界广泛采用。该建议适应R /生物导体，以满足多测癌基因组实验的概念和计算复杂性的日益增长。首先，我们开发软件容器，用于协调的表示，操作以及从多个癌症基因组测定中的异质得出数据的转换。然后将这些容器扩展以管理非常大的主要数据资源。为了促进将本地实验结果与主要的公共癌症基因组学实验数据集和注释相结合，我们重新包装公共资源，并提供基于软件和云的设施，以便从R/Bioconductor内部易于快速的程序化访问。这极大地简化了癌症基因组分析任务，否则需要大量易于错误的个人努力。最后，我们提供软件基础架构，以使用并行和迭代方法启用高通量计算。在公共实验和注释的背景下，操纵多测癌基因组实验的能力，了解个人实验结果，以及在建立良好的计算环境中改善高通量计算性能的设施，从而极大地增强了分析和理解大型多层癌症基因组基因组基因组基因组实验的机会。