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Federation in genomics pipelines: techniques and challenges

机译:基因组学管道中的联合:技术和挑战

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摘要

Federation is a popular concept in building distributed cyberinfrastructures, whereby computational resources are provided by multiple organizations through a unified portal, decreasing the complexity of moving data back and forth among multiple organizations. Federation has been used in bioinformatics only to a limited extent, namely, federation of datastores, e.g. SBGrid Consortium for structural biology and Gene Expression Omnibus (GEO) for functional genomics. Here, we posit that it is important to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular bioinformatics portals, with fast-increasing data volumes and increasing processing requirements. A prime example, and one that we discuss here, is in genomics and metagenomics. It is critical that the processing of the data be done without having to transport the data across large network distances. We exemplify our design and development through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline. Currently, it is hosted completely at Argonne National Laboratory. However, through a recently started collaborative National Institutes of Health project, we are taking steps toward federating this infrastructure. Being a widely used resource, we have to move toward federation without disrupting 50 K annual users. In this article, we describe the computational tools that will be useful for federating a bioinformatics infrastructure and the open research challenges that we see in federating such infrastructures. It is hoped that our manuscript can serve to spur greater federation of bioinformatics infrastructures by showing the steps involved, and thus, allow them to scale to support larger user bases.
机译:联盟是在构建分布式网络基础架构中流行的概念,由此多个组织通过统一的门户提供计算资源,从而降低了在多个组织之间来回移动数据的复杂性。联盟仅在有限的程度上用于生物信息学,即数据存储的联盟,例如数据存储。 SBGrid联盟用于结构生物学,而Gene Expression Omnibus(GEO)用于功能基因组学。在这里,我们认为,重要的是要联合计算资源(CPU,GPU,FPGA等)和数据存储以支持流行的生物信息学门户,同时数据量迅速增加且处理要求不断提高。基因组学和宏基因组学是一个很好的例子,我们在这里讨论。至关重要的是,无需在较大的网络距离上传输数据即可完成数据处理。我们通过最流行的宏基因组学分析流水线宏基因组学RAST(MG-RAST)的经验来举例说明我们的设计和开发。目前,它完全由Argonne国家实验室托管。但是,通过最近启动的一项合作的美国国立卫生研究院项目,我们正在采取措施,将这一基础设施联合起来。作为一种广泛使用的资源,我们必须朝着联盟发展,而不会中断每年5万名用户。在本文中,我们描述了对联合生物信息学基础设施将有用的计算工具,以及在联合此类基础设施时看到的开放研究挑战。希望我们的手稿能够通过显示所涉及的步骤来促进生物信息学基础设施的更大联盟,从而使它们能够扩展规模以支持更大的用户群。

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