Accelerating Cancer Genomics with Cloud-scale Bioconductor

利用云规模 Bioconductor 加速癌症基因组学

• 批准号: 9478159
• 负责人: VINCENT JAMES CAREY
• 金额: $ 43.87万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9478159
• 关键词: Acceleration; Achievement; Address; Adopted; Animal Cancer Model; Architecture; Awareness; Base Sequence; Bioconductor; Bioinformatics; Biological Assay; Biology; Clinical Data; Clinical Trials; Cloud Computing; Collaborations; Collection; Computational Biology; Computer software; Computers; Data; Data Analyses; Data Analytics; Data Storage and Retrieval; Development; Ecosystem; Engineering; Environment; Eye; Fostering; Foundations; Genomic Data Commons; Genomics; Goals; Hand; Harvest; Informatics; Institutes; Intuition; Language; Leadership; Letters; Machine Learning; Malignant Neoplasms; Methodology; Methods; Modality; Molecular; Molecular Profiling; Motivation; Mutation Spectra; Neural Network Simulation; Outcome; Output; Pathway Analysis; Pathway interactions; Patients; Performance; Physicians; Pilot Projects; Plant Roots; Process; Publications; Published Comment; Recurrence; Reproducibility; Research; Research Infrastructure; Research Personnel; Resistance; Resolution; Resources; Scheme; Scientist; Software Engineering; Software Tools; Source; Statistical Models; Structure; System; Systems Biology; TensorFlow; Testing; The Cancer Genome Atlas; Time; Update; Validation; Variant; Work; actionable mutation; analytical method; application programming interface; base; cancer genome; cancer genomics; cancer subtypes; cancer type; cloud based; computing resources; data archive; data format; data submission; design; experimental study; flexibility; genome analysis; genome-wide; genomic data; genomics cloud; high dimensionality; novel strategies; patient variability; portability; prototype; response; software development; spelling; tool; treatment response; treatment strategy; tumor; tumor initiation; usability

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.

项目摘要 生物导体项目的源于认识到高度，严格且可重现的高度分析 当统计学家，生物学家和计算机科学家在努力中努力时，可以实现维度数据 透明和精心设计的方式。该项目通过云规模生物加速癌症基因组学 导体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。该提案是基于在支持十五年中证明高效的策略 用于Biocon-计算生物学基因组量表分析的协作和精心设计的软件 - 导管项目，基于高度便携式和广泛采用的R语言和环境进行数据分析。 在AIM 1中，我们开发建筑和基础架构，以可靠地收集大量的基于云的表示 比例癌症基因组研究（例如癌症基因组图集），创造了形式的高性能工作流量 用于处理和解释癌症基因组分析，并提供包装和数据分配方案 将数据移至云以进行可扩展分析。在目标2中，我们创建并支持独立创建 直观且与癌症相关的界面组件支持可再现的交互式探索和分析 使用Rstudio的设施。在AIM 3中，我们更新和概括了生物导体mlinterfaces metapackage 使用目标1和2中设计的面向癌症的策略和设施支持先进的机器学习。 我们的建议将使大量将利用云资源的癌症研究人员， 大概是r即将到来的，通过嫁给以云中为中心的策略进行数据归档和查询的优势 解决生物导体开发和分析能力的优势。我们有支持信 从该项目的三个NCI癌症云试点项目的领导。