Kim Baekdoo, Ali Thahmina, Dong Changsu, Lijeron Carlos, Mazumder Raja, Wultsch Claudia, Krampis Konstantinos
1 Weill Cornell Medicine, Belfer Research Building, New York, New York.
2 Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, District of Columbia.
J Comput Biol. 2019 Mar;26(3):280-284. doi: 10.1089/cmb.2018.0218. Epub 2019 Jan 17.
The availability of low-cost small-factor sequencers, such as the Illumina MiSeq, MiniSeq, or iSeq, have paved the way for democratizing genomics sequencing, providing researchers in minority universities with access to the technology that was previously only affordable by institutions with large core facilities. However, these instruments are not bundled with software for performing bioinformatics data analysis, and the data analysis can be the main bottleneck for independent laboratories or even small clinical facilities that consider adopting genomic sequencing for medical applications. To address this issue, we have developed miCloud, a bioinformatics platform that enables genomic data analysis through a fully featured data analysis cloud, which seamlessly integrates with genome sequencers over the local network. The miCloud can be easily deployed without any prior bioinformatics expertise on any computing environment, from a laboratory computer workstation to a university computer cluster. Our platform not only provides access to a set of preconfigured RNA-Seq and CHIP-Seq bioinformatics pipelines, but also enables users to develop or install new preconfigured tools from the large selection available on open-source online Docker container repositories. The miCloud built-in analysis pipelines are also integrated with the Visual Omics Explorer framework (Kim et al., 2016), which provides rich interactive visualizations and publication-ready graphics from the next-generation sequencing data. Ultimately, the miCloud demonstrates a bioinformatics approach that can be adopted in the field for standardizing genomic data analysis, similarly to the way molecular biology sample preparation kits have standardized laboratory operations.
低成本小型因子测序仪的出现,如Illumina MiSeq、MiniSeq或iSeq,为基因组学测序的普及铺平了道路,使少数族裔大学的研究人员能够使用以前只有拥有大型核心设施的机构才能负担得起的技术。然而,这些仪器并未捆绑用于执行生物信息学数据分析的软件,而数据分析可能是独立实验室甚至是考虑将基因组测序用于医学应用的小型临床机构的主要瓶颈。为了解决这个问题,我们开发了miCloud,这是一个生物信息学平台,通过功能齐全的数据分析云实现基因组数据分析,该云通过本地网络与基因组测序仪无缝集成。miCloud可以轻松部署,无需任何生物信息学专业知识,可在任何计算环境中使用,从实验室计算机工作站到大学计算机集群。我们的平台不仅提供对一组预配置的RNA-Seq和CHIP-Seq生物信息学管道的访问,还允许用户从开源在线Docker容器存储库中大量可用的工具中开发或安装新的预配置工具。miCloud内置的分析管道还与Visual Omics Explorer框架(Kim等人,2016年)集成,该框架可从下一代测序数据中提供丰富的交互式可视化和可用于发表的图形。最终,miCloud展示了一种生物信息学方法,该方法可在该领域采用,以实现基因组数据分析的标准化,类似于分子生物学样本制备试剂盒对实验室操作进行标准化的方式。