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

基本信息

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
项目类别：
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

项目摘要

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）：该提案开发可扩展的 R / Bioconductor 软件基础设施和数据资源，以集成复杂、异质和大型癌症基因组实验。基因组检测成本的下降有利于从一组临床样本中收集多种数据类型（例如基因和转录本表达、结构变异、拷贝数、甲基化和 microRNA 数据）。此外，癌症基因组图谱 (TCGA) 等大型联盟活动现在提供了大量资源。现有的分析流程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数据的多种基因组分析进行综合分析的工具。 R / Bioconductor 历来提供标准化的基因组数据结构和注释，并在癌症基因组学研究界得到广泛采用。该提案采用 R / Bioconductor 来满足多分析癌症基因组实验不断增加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测定的异质衍生数据。然后扩展这些容器来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集成，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，否则需要大量且容易出错的个人努力。最后，我们提供软件基础设施，以使用并行和迭代方法实现高吞吐量计算。操纵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，以及在完善的计算环境中改进高通量计算性能的设施，极大地增强了分析和理解大型癌症的机会。多分析癌症基因组实验。