Calabrese Barbara, Cannataro Mario
Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy.
Methods Mol Biol. 2016;1375:25-39. doi: 10.1007/7651_2015_236.
High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and thus, it may represent the key technology for facing those issues. In fact, in the recent years it has been adopted for the deployment of different bioinformatics solutions and services both in academia and in the industry. Although this, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This chapter reviews main academic and industrial cloud-based bioinformatics solutions; with a special focus on microarray data analysis solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.
诸如微阵列、质谱分析和下一代测序等高通量平台正在产生越来越多的组学数据，这些数据需要大量的数据存储和计算能力。云计算提供了大规模可扩展的计算和存储、数据共享以及随时随地按需访问资源和应用程序的功能，因此，它可能是解决这些问题的关键技术。事实上，近年来，云计算已被学术界和工业界用于部署不同的生物信息学解决方案和服务。尽管如此，云计算在数据安全和隐私方面存在若干问题，在分析患者数据（如个性化医疗中）时，这些问题尤为重要。本章回顾了主要的基于云计算的学术和工业生物信息学解决方案；特别关注微阵列数据分析解决方案，并强调了使用此类平台存储和分析患者数据时的主要问题。
