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利用云计算构建生物医学信息学工作流程的 11 个快速技巧。

Eleven quick tips for architecting biomedical informatics workflows with cloud computing.

机构信息

Institute for Biomedical Informatics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

出版信息

PLoS Comput Biol. 2018 Mar 29;14(3):e1005994. doi: 10.1371/journal.pcbi.1005994. eCollection 2018 Mar.

Abstract

Cloud computing has revolutionized the development and operations of hardware and software across diverse technological arenas, yet academic biomedical research has lagged behind despite the numerous and weighty advantages that cloud computing offers. Biomedical researchers who embrace cloud computing can reap rewards in cost reduction, decreased development and maintenance workload, increased reproducibility, ease of sharing data and software, enhanced security, horizontal and vertical scalability, high availability, a thriving technology partner ecosystem, and much more. Despite these advantages that cloud-based workflows offer, the majority of scientific software developed in academia does not utilize cloud computing and must be migrated to the cloud by the user. In this article, we present 11 quick tips for architecting biomedical informatics workflows on compute clouds, distilling knowledge gained from experience developing, operating, maintaining, and distributing software and virtualized appliances on the world's largest cloud. Researchers who follow these tips stand to benefit immediately by migrating their workflows to cloud computing and embracing the paradigm of abstraction.

摘要

云计算已经彻底改变了不同技术领域的硬件和软件的开发和运营,但尽管云计算提供了众多重要优势,学术生物医学研究仍落后不前。采用云计算的生物医学研究人员可以在降低成本、减少开发和维护工作量、提高可重复性、方便共享数据和软件、增强安全性、水平和垂直可扩展性、高可用性、蓬勃发展的技术合作伙伴生态系统等方面获益。尽管基于云的工作流程提供了这些优势,但学术界开发的大多数科学软件并未利用云计算,并且必须由用户将其迁移到云端。在本文中,我们介绍了在计算云中构建生物医学信息学工作流程的 11 个快速技巧,这些技巧是从在世界上最大的云中开发、操作、维护和分发软件和虚拟设备中获得的经验中提炼出来的。遵循这些技巧的研究人员可以通过将其工作流程迁移到云计算并采用抽象范式来立即受益。

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本文引用的文献

1
Bringing numerous methods for expression and promoter analysis to a public cloud computing service.
Bioinformatics. 2018 Mar 1;34(5):884-886. doi: 10.1093/bioinformatics/btx692.
2
The Adoption of Cloud Computing in the Field of Genomics Research: The Influence of Ethical and Legal Issues.
PLoS One. 2016 Oct 18;11(10):e0164347. doi: 10.1371/journal.pone.0164347. eCollection 2016.
3
The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences.
PLoS Biol. 2016 Jan 11;14(1):e1002342. doi: 10.1371/journal.pbio.1002342. eCollection 2016 Jan.
4
Data analysis: Create a cloud commons.
Nature. 2015 Jul 9;523(7559):149-51. doi: 10.1038/523149a.
5
Reproducibility in science: improving the standard for basic and preclinical research.
Circ Res. 2015 Jan 2;116(1):116-26. doi: 10.1161/CIRCRESAHA.114.303819.
6
Ten simple rules for reproducible computational research.
PLoS Comput Biol. 2013 Oct;9(10):e1003285. doi: 10.1371/journal.pcbi.1003285. Epub 2013 Oct 24.
7
ProteoCloud: a full-featured open source proteomics cloud computing pipeline.
J Proteomics. 2013 Aug 2;88:104-8. doi: 10.1016/j.jprot.2012.12.026. Epub 2013 Jan 8.
8
Reproducible research in computational science.
Science. 2011 Dec 2;334(6060):1226-7. doi: 10.1126/science.1213847.
9
A vision for a biomedical cloud.
J Intern Med. 2012 Feb;271(2):122-30. doi: 10.1111/j.1365-2796.2011.02491.x.
10
Biomedical cloud computing with Amazon Web Services.
PLoS Comput Biol. 2011 Aug;7(8):e1002147. doi: 10.1371/journal.pcbi.1002147. Epub 2011 Aug 25.

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