Accelerating Cancer Genomics with Cloud-scale Bioconductor

项目总结 BioConductor项目植根于对高效、严格和可重复性的高密度fi分析的认识 当统计学家、生物学家和计算机科学家联合努力时，就可以获得维度数据 透明和精心设计的方式。利用云规模生物加速癌症基因组学的项目 Conductor设计了使用云计算对癌症数据进行基因组规模分析的新方法 环境。该提案基于在fi14年的支持中被证明非常有效的策略 用于计算生物学中的基因组规模分析的协作和精心设计的软件- Dductor项目，基于高度可移植和被广泛采用的R语言和环境进行数据分析。 在目标1中，我们开发了架构和基础设施，用于可扩展地获取基于云的大型 大规模癌症基因组研究，如癌症基因组图谱，创建正式的高性能工作flOWS 用于处理和解释癌症基因组分析，并提供打包和数据分发方案 用于将数据移动到云中以进行可扩展的分析。在目标2中，我们创建并支持独立创建 支持可重现的交互式探索和分析的直观且与癌症相关的界面组件 使用RStudio的设施。在目标3中，我们将BioConductor MLInterages元背包更新和推广到 使用目标1和目标2中设计的面向癌症的策略和设施，支持先进的机器学习。 我们的提议将使大量的癌症研究人员受益，他们将利用云资源， 可能与R近在咫尺，通过结合以云为中心的数据归档和查询策略的优势 解决方案，归因于生物导体的开发和分析能力的优势。我们有支持信 来自该项目的三个NCI癌症云试点项目的领导。

PROJECT SUMMARY The Bioconductor project is rooted in recognition that efficient, rigorous, and reproducible analysis of high- dimensional data can be achieved when statisticians, biologists, and computer scientists federate efforts in a transparent and carefully engineered way. The project Accelerating Cancer Genomics with Cloud-scale Bio- conductor devises new approaches to carrying out genome-scale analysis of cancer data using cloud computing environments. The proposal is based on strategies that have proven highly effective in fifteen years of supporting collaborative and carefully engineered software for genome scale analysis in computational biology in the Biocon- ductor project, based on the highly portable and widely adopted R language and environment for data analysis. In Aim 1 we develop architecture and infrastructure for scalably harvesting cloud-based representations of large- scale cancer genome studies such as The Cancer Genome Atlas, creating formal high-performance workflows for processing and interpreting cancer genome analyses, and providing packaging and data distribution schemes for moving data to the cloud for scalable analysis there. In Aim 2 we create and support independent creation of intuitive and cancer-relevant interface components supporting reproducible interactive exploration and analysis using the facilities of Rstudio. In Aim 3 we update and generalize the Bioconductor MLInterfaces metapackage to support advanced machine learning using the cancer-oriented strategies and facilities devised in Aims 1 and 2. Our proposal will benefit large numbers of cancer researchers who will be taking advantage of cloud resources, probably with R close to hand, by marrying strengths of cloud-centric strategies for data archiving and query resolution, to the strengths of Bioconductor development and analysis capabilities. We have letters of support from the leadership of the three NCI Cancer Cloud Pilot projects for this project.