A Federated Galaxy for user-friendly large-scale cancer genomics research

项目摘要 癌症研究现在是一个数据驱动的学科，但只有少数癌症研究人员是数据科学家。这严重限制了我们有效研究和治疗这种疾病的能力。我们的项目的深远意义在于联合不同的数据和计算资源，以便为计算癌症研究提供统一的分析平台。 我们将扩展流行的科学工作台Galaxy（galaxyproject.org），以便它可以与癌症研究人员使用和需要的分布式数据和计算资源集成，包括NCI癌症研究数据共享（NCR DC）中的资源。我们的Federated Galaxy系统将允许用户跨多个资源无缝访问NCR DC数据。它将支持多个分析方案，以适应各个研究人员的技能和计算要求。 该项目的目标是： 目标1.扩展Galaxy，用于处理分布式癌症基因组学和表型数据。这将使Galaxy用户能够访问公共和私人癌症数据，而无论其实际物理位置如何。将采用最佳做法办法访问限制性数据集。 目标二。使用共享工作流表示增强Galaxy的上下文感知、分布式癌症基因组学分析。这将使Galaxy用户能够在不同的云上运行基因组学分析，最终减少与分析相关的时间，成本和数据传输。 目标3.将Federated Galaxy应用于精准肿瘤学研究。为此目的开发的工作流程将利用目的1和2中的技术，对机器学习算法进行基准测试，以预测肿瘤表型和药物反应。交互式报告将总结基准测试结果，并利用ITCR可视化技术深入研究结果。 我们的系统将为分布式癌症数据集提供一个单一的访问点，并将使这些数据能够在一个单一的门户网站内进行分析，以满足多种分析场景，并利用不同的计算资源。最后，为NCR DC构建的以云为中心的Galaxy将大大增加与GDC和NCR DC合作的用户社区。这是因为Galaxy带来了一个充满活力的全球用户和开发人员社区，其中有数万名科学家。这些人将帮助调整GDC和NCR DC中的其他资源，以满足现实分析场景的需求，并将丰富癌症研究人员可用的工具集。

Project Summary Cancer research is now a data-driven discipline, but only a minority of cancer researchers are data scientists. This severely restricts our ability to effectively study and cure the disease. The far reaching significance of our project is in federating disparate data and computational resources in order to provide a unifying analysis platform for computational cancer research. We will extend the popular scientific workbench Galaxy (https://galaxyproject.org) so that it can integrate with distributed data and compute resources used and needed by cancer researchers, including those resources in the NCI Cancer Research Data Commons (NCR DC). Our Federated Galaxy system will allow users to seamlessly access NCR DC data across multiple resources. It will support multiple analysis scenarios tuned to skills and computational requirements of individual researchers. The aims of this project are: Aim 1. Extend Galaxy for working with distributed cancer genomics and phenotypic data. This will enable Galaxy users to access both public and private cancer data regardless of their actual physical location. Best-practice approaches will be used for accessing restricted datasets. Aim 2. Enhance Galaxy for context-aware, distributed cancer genomics analyses using shared workflow representations. This will enable Galaxy users to run genomics analyses on different clouds, ultimately reducing the time, cost, and data transfer associated with analyses. Aim 3. Apply Federated Galaxy to precision oncology research. Workflows developed in this aim will leverage the technologies in Aims 1 and 2 to benchmark machine learning algorithms for predicting tumor phenotype and drug response. Interactive reports will summarize benchmarking results and utilize ITCR visualizations for deep dives into results. Our system will provide a singular access point to distributed cancer datasets and will enable these data to be analyzed within a single portal in a way that satisfies multiple analysis scenarios and utilizes diverse computational resources. Finally, a cloud-centric Galaxy built for the NCR DC will substantially grow the community of users working with the GDC and the NCR DC. This is because Galaxy brings with itself a vibrant world-wide community of users and developers, which numbers tens of thousands of scientists. These individuals will help to tune the GDC and other resources within the NCR DC to the needs of real-life analysis scenarios and will enrich the set of tools accessible to cancer researchers.