Cancer Genomics:Integrative and Scalable Solutions in R / Bioconductor

描述（由申请人提供）：该提案开发了可扩展的R / Bioconductor软件基础设施和数据资源，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多种数据类型的收集（例如，基因和转录本表达、结构变异、拷贝数、甲基化和微小RNA数据）。此外，大量的资源，现在可从大型财团活动，如癌症基因组图谱（TCGA）。现有的分析管道专注于处理特定的数据类型，因此迫切需要对本地生成或公开可用数据的多个基因组测定进行综合分析的工具。R / Bioconductor在历史上提供了标准化的基因组数据结构和注释，在癌症基因组学研究界得到了广泛采用。该建议适应R / Bioconductor，以满足多检测癌症基因组实验日益增加的概念和计算复杂性。我们开始通过开发软件容器协调表示，操作和转换的异构衍生数据从多个癌症基因组检测。然后扩展这些容器以管理非常大的主数据资源。为了促进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的整合，我们重新包装了公共资源，并提供了软件和基于云的设施，以便从R/Bioconductor内部轻松快速地进行程序访问。这大大简化了癌症基因组分析任务，否则需要大量的，容易出错的个人努力。最后，我们提供了软件基础设施，使高吞吐量计算使用并行和迭代的方法。操纵多测定癌症基因组实验、在公共实验和注释的背景下理解个体实验结果的能力，以及在完善的计算环境中用于改进的高通量计算性能的设施，极大地增强了分析和理解大型多测定癌症基因组实验的机会。

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.