ABI Development: Leveraging NSF-funded national cyberinfrastructure to spearhead biological discovery with Galaxy

由于测序、成像和其他技术带来的生物数据量迅速增加，生命科学领域的数据处理需求现在与物理和工程学科不相上下。重要的是，生物学中数据生成的分布式特性使这种情况更具挑战性。如今，几乎找不到一个没有多台高通量DNA测序仪的研究机构或大学，人们经常提到生物学中的“数据危机”。联邦机构，尤其是美国国家科学基金会，正在大力投资网络基础设施，支持高性能计算（HPC）资源的开发，如极限科学与工程发现环境（XSEDE）。然而，在很大程度上，这些资源对生物研究人员来说仍然是未知的，他们绝大多数仍然依赖于脆弱的内部计算。该项目的目标是确保有效利用已投资于国家计算基础设施发展的联邦资金。该项目将扩展Galaxy软件平台，以利用现有的NSF硬件资源，为以前无法充分利用这些资源的生物学研究人员增加现有基础设施的价值。该项目将采用综合方法，满足实验科学家、工具开发人员和高性能计算系统（HPC）管理员的需求。对国家计算基础设施的访问将得到扩展，因此Galaxy将作为现有异构环境（如XSEDE）或单个系统（如Jetstream）的中间件接口。将基于德克萨斯高级计算中心（TACC）、XSEDE、PSC和印第安纳大学的试点项目开发优化Galaxy作为研究人员和现有HPC之间的纽带所必需的软件组件。(2)将利用XSEDE资源实现交互式数据探索和可视化，以扩大Galaxy当前动态科学数据分析的能力。与交互分析环境（如Jupyter或RStudio）的集成将允许使用通用脚本语言操作和创建Galaxy数据集。利用XSEDE资源将使Galaxy的交互式环境和可视化分析能够扩展到大型数据集和复杂的工作流程。(3)可持续的培训和推广将侧重于创建和传播课程，使研究人员能够学习分析大型数据集所需的技能。创建预先配置的基础设施组件，用于举办研讨会，并为本科生和研究生的面对面和在线课程开发模块，这将扩大当前的教育组合，以支持越来越多的Galaxy用户，包括生命科学以外的学科，如自然语言处理。该项目的成果将在http://galaxyproject.org和https://github.com/galaxyproject上公布。

Due to the rapidly increasing volume of biological data from sequencing, imaging, and other technologies, data processing needs in the Life Sciences are now on par with physical and engineering disciplines. Importantly, the distributed nature of data generation in biology makes this situation even more challenging. Today one can hardly find a research institution or university without multiple high-throughput DNA sequencing machines, and there are often references to a "data crisis" in biology. Federal agencies, and the NSF in particular, are investing heavily in cyberinfrastructure by supporting development of high performance computing (HPC) resources such as the Extreme Science and Engineering Discovery Environment (XSEDE). Yet to a large extent, these resources remain unknown to biological researchers who overwhelmingly continue to rely on fragile in-house computation. The goal of this project is to ensure effective utilization of federal funds that have been invested into development of the national computing infrastructure. This project will extend the Galaxy software platform to leverage existing NSF hardware resources, increasing the value of existing infrastructure for biology researchers that were previously unable to take full advantage of these resources.This project will follow a comprehensive approach that addresses the needs of experimental scientists, tool developers, and administrators of high performance compute systems (HPC). Access to national compute infrastructure will be expanded so that Galaxy will function as a middleware interface to existing heterogeneous environments such as XSEDE or individual systems such as Jetstream. Software components necessary to optimize Galaxy as a link between researchers and existing HPC will be developed based on pilot projects with the Texas Advanced Computing Center (TACC), XSEDE, PSC, and Indiana University. (2) XSEDE resources to enable interactive data exploration and visualization will be leveraged to expand Galaxy's current capacity for dynamic scientific data analysis. Integration with Interactive Analysis Environments, such as Jupyter or RStudio will allow manipulation and creation of Galaxy datasets using common scripting languages. Taking advantage of XSEDE resources will enable Galaxy's interactive environments and visual analytics to scale to large datasets and sophisticated workflows. (3) Sustainable training and outreach will focus on creating and disseminating curricula that enable investigators to learn skills needed to analyze large datasets. Creation of pre-configured infrastructure components for running workshops and develop modules for undergraduate and graduate face-to-face and on-line classes will expand the current educational portfolio to scale support for increasing numbers of Galaxy users, including disciplines beyond life sciences such as Natural Language Processing. Outcomes of this project will be available at http://galaxyproject.org and https://github.com/galaxyproject.