Cancer Genomics:Integrative and Scalable Solutions in R / Bioconductor

描述（由申请人提供）：该提案开发可扩展的R / Bioconductor软件基础设施和数据资源，以整合复杂，异构和大型癌症基因组实验。基因组分析成本的下降有助于从一组临床标本中收集多种数据类型（例如，基因和转录物表达、结构变异、拷贝数、甲基化和microRNA数据）。此外，从诸如癌症基因组图谱（TCGA）这样的大型联盟活动中可以获得大量资源。现有的分析管道侧重于特定数据类型的处理，因此迫切需要对本地生成或公开可用的数据进行多种基因组分析的综合分析工具。R / Bioconductor历来提供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广泛采用。该方案采用R / Bioconductor来满足越来越多的癌症基因组实验的概念和计算复杂性。我们首先开发软件容器，用于协调表示、操作和转换来自多种癌症基因组分析的异构衍生数据。然后对这些容器进行扩展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公共癌症基因组学实验数据集和注释的整合，我们重新打包公共资源，并提供软件和基于云的设施，以便从R/Bioconductor内部轻松快速地进行编程访问。这极大地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，我们提供了使用并行和迭代方法实现高吞吐量计算的软件基础设施。操作多分析癌症基因组实验的能力，在公共实验和注释的背景下理解个体实验结果的能力，以及在一个完善的计算环境中提高高通量计算性能的设施，极大地提高了分析和理解大型多分析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.