Investigation of Cloud Computing to Support Data-Parallel Health Research

描述(由申请人提供)：我们将组成一个由印第安纳大学计算机科学家、生物学家和生物信息学家组成的多学科团队，开发和部署新的大规模计算基础设施和工具，使基础健康研究成为可能。我们的研究将调查云计算架构对大规模计算生物学的影响，特别是广泛遇到的数据并行问题，包括但不限于DNA序列分析。围棋资金将用于建立基于云的计算生命科学的新领域。云计算目前以亚马逊网络服务、微软Azure和其他商业努力为代表。然而，许多大学(包括印第安纳大学)正在建立研究云部署，以解决两个一般问题：基础设施：云提供简单的Web服务编程接口，允许科学家创建计算集群和使用高度可靠的数据存储。也就是说，云提供了一种外包计算基础设施的方式。运行时：云系统特别适合运行大规模信息检索问题。这些数据并行问题涉及复制的顺序命令管道，这些命令处理被分成多个部分的非常大的数据集。示例技术包括微软Dryad和阿帕奇Hadoop。在这份提案中，我们将与微软研究院合作，后者目前正在将Dryad从一个研究项目转变为一个强大的工具。我们分析了各种各样的运行状况研究问题，并表明它们可以从云基础设施和运行时中受益。云为研究小组提供了一种外包计算、存储和网络的方法，并在健康研究中的数据并行问题上实现高性能。我们团队的研究工作(许多由NIH资助)代表了广泛的应用，包括a)基于序列的转录组图谱，b)用于突变图谱的基因组重测序，c)元基因组分析，d)基因组注释，e)比较基因组学，和f)群体基因组学h)患者健康记录中的高级并行数据挖掘。处理大规模数据是联合这些努力的共同问题。 公共卫生相关性：我们建议调查和开发一个独特的云计算研究基础设施，该基础设施将对几个不同的生命科学研究领域产生非常大的影响。我们的重点是大规模的数据并行分析问题，这些问题是由来自短读基因测序设备和其他来源的大量数据造成的。我们将与几个现有的生物和生物医学项目合作，开发和展示我们的基础设施。

DESCRIPTION (provided by applicant): We will form a multidisciplinary team of Indiana University computer scientists, biologists, and bioinformaticians to develop and deploy new large-scale computing infrastructure and tools that will enable fundamental health research. Our research will investigate the impact of Cloud computing architectures on large-scale computational biology, particularly widely encountered, "data parallel" problems including but not limited to DNA sequence analysis. GO funds will be used to establish the new field of Cloud-based computational life science. Cloud computing is currently typified by Amazon Web Services, Microsoft Azure, and other commercial efforts. However, many universities (including Indiana University) are in the process of establishing research Cloud deployments that will address two general problems: Infrastructure: Clouds provide simple Web service programming interfaces that allows scientists to create computing clusters and use highly reliable data storage. That is, Clouds provide a way to outsource computing infrastructure. Runtimes: Cloud systems are particularly appropriate for running large-scale information retrieval problems. These data-parallel problems involve pipelines of replicated, sequential commands that process very large data sets divided into many pieces. Example technologies include Microsoft Dryad and Apache Hadoop. In this proposal, we will partner with Microsoft Research, which is currently converting Dryad from a research project to a robust tool. We have analyzed a wide variety of health research problems and have shown that they can benefit from Cloud infrastructure and runtimes. Clouds provide research groups with a way to outsource computing, storage, and networking and to achieve high performance on data-parallel problems in health research. Our team's research efforts (many NIH funded) represent a wide range of applications, including a) sequence-based transcriptome profiling, b) genome re-sequencing for mutation mapping, c) metagenomics analysis, d) genome annotation, e) comparative genomics, and f) population genomics h) advanced parallel datamining in patient health records. Processing large-scale data is the common problem uniting these efforts. PUBLIC HEALTH RELEVANCE: We propose to investigate and develop a unique Cloud computing research infrastructure that will have a very large impact on several different life science research areas. Our focus is on the large-scale, data-parallel analysis problems that result from the deluge of data from short-read gene sequencing devices and other sources. We will develop and demonstrate our infrastructure in collaboration with several existing biological and biomedical projects.