High-performance Computing System for Bioinformatics

描述(申请人提供)：计算生物学的爆炸性增长使得研究机构很难跟上用户对不断增长的计算能力的需求。生物学家面临的复杂情况来自两个方面的发展。首先，新技术产生了海量数据。当然，这使得生物学研究有可能将其范围扩大到整个基因组、细胞甚至生物体水平，但代价是使现有的数据分析方法和资源负担过重。其次，算法和分析方法在计算上变得更加密集，部分原因是为了应对数据丰富带来的机遇，部分原因是为了管理似乎隐含在基因组数据集中的令人遗憾的信噪比。此外，“系统生物学”的出现导致了计算工作的日益复杂，因为系统生物学最终寻求在计算机中对生物现象进行建模。实际上，基因组科学家和系统生物学家的主要工具之一--实际上也是最灵活的--是高性能计算机，因为它是理解来自高通量测序仪、基因表达微阵列设备、质谱仪等的海量数据的必要工具。在高性能集群计算机上，许多研究人员正在利用基本的“作业级并行”，即单个用户可以同时在数百台计算机上运行多个作业(或作业的独立子部分)。通常，这是以计算“参数空间研究”的形式进行的，即在数十、数百和数千个不同的输入集合上运行相同的应用程序。例如，模拟调控区域的进化需要多次运行，在这些运行中，调节短调控基序的大小和数量。基因调控网络的预测需要多个模拟，其中不同的细胞类型和不同的组织区域被修改。对细胞群体中基因表达动态的模拟也必须多次运行，以解释“细胞噪声”，并获得现象的全面图景。这种重复计算的需要使得集群计算成为解决这些问题的一种有吸引力的方法。我们的方案需要94台高能效计算服务器和大约8 TB(可用)的高速数据存储以及与之匹配的灾难恢复存储。该设备将使用Sun Grid Engine投入运行，这是一种软件应用程序，可以协调计算资源，使单独的机器充当一个集群计算仪器。生物信息学软件工具，以及定制的应用程序，可供研究人员在设备上使用。 与公共卫生相关：下一代仪器使获取基因组数据变得更便宜、更高效，新技术有望极大地提高用于生物医学研究和转化医学的数据的分辨率和丰富性。这股数据洪流需要同样强大和灵活的分析和信息创建工具，有效地将高通量数据生产者与高性能计算工具相匹配以进行分析。我们建议创建一个良好集成的计算系统，在计算能力上匹配来自产生基因组数据的仪器的海量数据流。

DESCRIPTION (provided by applicant): The explosive growth of computational biology has made it difficult for research organizations to keep pace with users' demands for ever-increasing computational power. The complications that biologists face come from two developments. First, new technologies generate huge amounts of data. Of course this makes it possible for biological investigations to broaden their scope to whole genome, cellular, and even organism levels, but at a cost of overtaxing existing methods and resources for data analysis. Second, algorithms and methods of analysis have become more computationally intensive, in part as a response to the opportunities that data richness has brought about and in part to manage the unfortunate signal-to-noise ratio that seem implicit in genomic datasets. Also, the emergence of "systems biology" has led to growing complication in computational work, since systems biology seeks eventually to model biological phenomena in silico. In effect, one of the major -- and indeed the most flexible -- instruments for genome scientists and systems biologists is the high performance computer, because it is an essential tool for making sense of the prodigious amounts of data already coming from high-throughput sequencers, gene expression microarray equipment, mass spectrometers, and the like. On high-performance cluster computers, many researchers are making use of basic "job-level parallelism" by which a single user may run multiple jobs (or independent sub-parts of jobs) on many hundreds of computers at once. Often, this is in the form of computational "parameter space studies" where the same application is run on tens, hundreds and thousands of different sets of inputs. Simulating the evolution of regulatory regions, for example, requires multiple runs in which the size and number of short regulatory motifs are tuned. The prediction of gene regulatory networks requires multiple simulations in which different cell types and different tissue regions are modified. Simulations of gene expression dynamics in populations of cells must also be run multiple times in order to account for "cellular noise" and get a comprehensive picture of the phenomena. This need for repeated computations makes cluster computing an attractive approach for these problems. Our proposal requests 94 power-efficient compute servers and about 8 terabytes (usable) high-speed data storage with matched disaster recovery storage. This equipment will be put into operation using Sun Grid Engine, a software application that coordinates computational resources so that individual machines function as one clustered computational instrument. Bioinformatic software tools, as well as custom-made applications, are available for researchers to use on the equipment. PUBLIC HEALTH RELEVANCE: Next-generation instruments have made acquiring genomic data inexpensive and ever more efficient, and new technologies promise to add greatly to the resolution and richness of data used for biomedical research and for translational medicine. This torrent of data needs equally powerful and flexible tools for analysis and information creation, in effect matching high-throughput data producers with high performance computational tools for analysis. We propose the creation of a well integrated computational system that matches in compute power the prodigious data flows from instruments producing genomic data.