Highly Parallel Supercomputing for Translational Research

描述（由申请人提供）：TGen和ASU的科学家正在通过NIH和其他来源资助的各种研究项目，开发和检查人类疾病的分子谱和疾病状态中涉及的基本途径。其重点是辨别用于疾病诊断和预后的复杂或简单的生物标志物集合，以及开发用于指导最佳治疗选择和识别新靶点的分子分类。正在分析的分子谱数据集涵盖阿尔茨海默氏症、自闭症、糖尿病、冠心病、恶性胶质瘤、黑色素瘤、胰腺癌、前列腺癌、结肠癌、多发性骨髓瘤和乳腺癌。TGen和ASU的科学家从几个计算角度研究分子谱，使用不同复杂程度的数学模型，所有这些都试图识别在分子病理学中起关键作用的基因和基因网络。每个观点都涉及基因-基因相互作用及其调控网络的某些方面，从而产生一个组合问题，其中计算解决方案受到计算机处理能力、内存大小和I/O带宽的限制。TGen和ASU都有活跃的计算生物学家，生物信息学家，生物统计学家，科学程序员和工程师，他们与生物医学和临床科学家密切合作，开发各种计算和统计工具，解决复杂的生物医学问题。在TGen和ASU开发的工具的实例包括以下：用于全基因组关联研究的基于池的分析; SNP连锁和覆盖分析;强特征选择算法;利用先验知识推断基因调控网络;基于马尔可夫链的基因调控网络模拟;基于微阵列和临床数据的细胞背景选择;大微阵列数据的聚类;鲁棒性分析;基于基因组关联的分析;基于基因组关联的 分类和特征选择算法的误差估计;用于显著性分析的排列测试;以及两个或更多个全基因组之间的串联阵列块的可视化。除了内部开发的工具外，TGen和ASU的科学家还使用各种开源计算工具，如PLINK，Haploview，STRUCTURE，MUMmer，mpiBLAST，NAMD等各种商业软件工具，如GeneGo MetaCore，Incidity，Gene Spring，Varia，Sequencer和Mutation Surveyer，也被科学家使用。正在分析的数据类型包括基因表达阵列、SNP、CGH、siRNA、序列和临床特征。许多内部和开源计算工具需要可扩展的并行处理能力和大量的处理器内存来检查解决方案空间的巨大复杂性。传统的单处理器或向量处理机不提供所需的计算性能来解决许多复杂的研究问题，TGen和ASU科学家面临的，在一个实际的计算机挂钟时间。由于高通量测量仪器允许科学家收集越来越大量的细粒度数据，生物医学科学家需要一个可扩展的并行超级计算机系统来探索大量复杂的数据空间，并执行更现实的系统分析。具有64位存储器架构和高速I/O的可扩展并行超级计算机系统允许科学家采用最佳分析方法，而在传统计算机系统上采用次优分析方法以避免最佳数学模型和计算算法所需的过度延长的计算机分析时间。对于许多计算问题，从磁盘存储子系统中阅读大的输入数据文件以及将大量的处理结果写入磁盘存储子系统是整个计算机分析时间中的瓶颈的来源。一种64位并行集群计算架构，具有高带宽I/O和 高速存储子系统将允许有效地开发和使用计算模型， 可以充分利用并行计算能力的算法。迄今为止，TGen和ASU科学家的成功是以牺牲时间为代价的。然而，受疾病影响的人没有时间。所要求的并行集群计算仪器将优化TGen和ASU研究人员的能力，以有效地满足他们的数据分析和系统建模需求，促进及时有效的生物医学发现，以改善人类健康。

DESCRIPTION (provided by applicant): TGen and ASU scientists are working on a variety of research projects funded through the NIH and other sources that develop and examine molecular profiles of human diseases and fundamental pathways involved in disease states. The focus is to discern complex or simple sets of biomarkers useful for disease diagnosis and prognosis, as well as to develop molecular classification for directing optimal therapeutic choice and identifying new targets. The molecular profile datasets being analyzed cover Alzheimer's, Autism, Diabetes, Coronary Heart Disease, Malignant Gliomas, Melanoma, Pancreatic Cancer, Prostate Cancer, Colon Cancer, Multiple Myeloma, and Breast Cancer. TGen and ASU scientists examine molecular profiles from several computational perspectives, using mathematical models with varying degrees of complexity, all of which attempt to identify genes and gene networks that play crucial roles in the molecular pathology. Each of the perspectives involves some aspect of gene-gene interactions and their regulatory networks, creating a combinatorial problem where computational solutions are limited by the computer's processing power, memory size, and I/O bandwidth. Both TGen and ASU have active groups of computational biologists, bioinformaticians, biostatisticians, scientific programmers and engineers, who are working closely with biomedical and clinical scientists to develop various computational and statistical tools that address complex biomedical questions. Examples of the tools developed at TGen and ASU include the following: pooling-based analysis for genome wide association studies; SNP linkage and coverage analysis; strong feature selection algorithm; inference of gene regulatory networks with prior knowledge; Markov Chain-based simulation of gene regulatory networks; selection of cellular context based on microarray and clinical data; clustering of large microarray data; robust error estimation of classification and feature selection algorithms; permutation tests for significance analysis; and visualization of tandem array blocks among two or more whole genomes. In addition to the in-house developed tools, TGen and ASU scientists use a variety of open-source computational tools, such as PLINK, Haploview, STRUCTURE, MUMmer, mpiBLAST, NAMD, etc. A variety of commercial software tools, such as GeneGo MetaCore, Ingenuity, Gene Spring, Varia, Sequencer, and Mutation Surveyer, are also being used by the scientists. The data-types that are being analyzed include gene expression arrays, SNPs, CGH, siRNA, sequences, and clinical features. Many of the in-house and open-source computational tools require scalable parallel processing power and large amount of processor memory to examine the enormous complexity of the solution space. Conventional uniprocessor or vector processing machines do not provide the computational performance needed to solve many of complex research problems, which TGen and ASU scientists face, within a practical computer wall-clock time. As high-throughput measurement instruments allow scientists to collect increasingly large amounts of finegrained data, a scalable parallel supercomputer system is required for biomedical scientists to explore large volumes of complex data space and perform systems analyses that are more realistic. A scalable parallel supercomputer system with a 64-bit memory architecture and high-speed I/O allows scientists to employ optimal analytical approaches, whereas suboptimal analytical approaches are employed on a conventional computer system to avoid prohibitively protracted computer analysis time needed for optimal mathematical models and computational algorithms. For many computational problems, reading large input data files from and writing a large amount of processed results to a disk storage subsystem is a source of bottleneck in the overall computer analysis time. A 64-bit parallel cluster computing architecture with a high-bandwidth I/O and high-speed storage subsystem will allow efficient development and use of computational models and algorithms that can take a full advantage of parallel computing power. The success of TGen and ASU scientists to date has come at the sacrifice of time. However, individuals affected with disease do not have the luxury of time. The requested parallel cluster-computing instrument will optimize TGen and ASU researchers' ability to meet their data analysis and systems modeling needs efficiently, fostering timely and effective biomedical discovery for improved human health.