Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud
Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud
复制标题
DOI：
10.1371/journal.pone.0140829
复制
发表时间：
2015-10-26
期刊：
影响因子：
3.7
通讯作者：
Lonie, Andrew
中科院分区：
文献类型：
--
作者：
Afgan, Enis;Sloggett, Clare;Lonie, Andrew
背景分析高通量基因组数据是一项复杂且计算密集的任务，通常需要在数据转换和可视化的连续阶段中捆绑在一起的众多软件工具和大量参考数据集。实现最佳实践基因组分析的计算平台理想地满足了一些要求，其中包括：与大型用户和参考数据集密切相关的各种分析和可视化工具；工作流平台(S)，通过一套灵活的界面实现可访问、可重复、可移植的分析；高度可用、可扩展的计算资源；以及在使用这些资源方面的灵活性和多功能性，以满足不同用户的需求和专业知识。对于研究人员来说，访问合适的计算平台可能是一个巨大的障碍，因为建立这样的平台需要在硬件、经验和专业知识方面进行大量的前期投资。结果我们设计并实现了基因组学虚拟实验室(GVL)，作为机器映像、云管理工具和在线服务的中间件层，使研究人员能够按需构建任意大小的计算集群，预先填充完全配置的生物信息学工具、参考数据集以及工作流程和可视化选项。该平台非常灵活，用户可以通过基于Web的(Galaxy、RStudio、IPython Notebook)或命令行界面进行分析，并根据需要添加/删除计算节点和数据资源。最佳实践教程和协议提供了从入门培训到实践的途径。GVL可以在基于OpenStack的澳大利亚研究云(http://nectar.org.au))和亚马逊网络服务云上获得。结论为基于云的基因组学虚拟实验室的设计与实现提供了一种蓝图。我们讨论范围、设计考虑因素以及技术和后勤限制，并通过我们的实施提供的一套服务和资源来探索为研究社区增加的价值。
BackgroundAnalyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise.ResultsWe designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic.ConclusionsThis paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.