High Performance Computational System to Support LCMS/Proteomics Analysis

描述(由申请人提供)：布法罗大学(UB)和地区合作伙伴罗斯威尔公园癌症研究所。和Hauptman-Wood-ward Research Inst.(HWI)加入了对基础设施的重大持续投资，以支持针对基础、应用和临床生物医学应用的蛋白质组研究。分布的区域仪器网络提供了使用最先进的液相色谱/质谱仪(LC/MS)仪器的途径。为我们的研究人员联盟生成蛋白质组学数据的关键仪器包括Waters LC-QTOF Premier(RPCI)、配备电子转移解离和多维纳米流动LC(UB制药科学仪器核心)的Thermo LTQ-XL线性离子陷阱，以及位于UB新的纽约州生物信息学和生命科学卓越中心(CBLS)的Thermo LTQ Orbitrap和纳米流动LC(CBLS)。额外的LC/MS仪器支持正在进行的蛋白质组学研究，并提供增强的定量能力。这些包括UB制药科学核心的四个LC/三重四极杆MS(两个API3000和两个Thermo Quantum Ultra EMR)、一个API3000和RPCI的Thermo Quantum Ultra。对于这个由美国国立卫生研究院支持的实验室组成的多机构联盟来说，对从基础和临床研究样本中获得的大型蛋白质组学数据集的计算分析是一个严重的瓶颈。由于研究的性质，使用SEQUEST分析实验数据可能需要在标准的高端工作站上进行数小时到数天的计算，这取决于预期的基质复杂性和多肽修饰、使用ICAT或iTRAQ等试剂来量化相对表达，以及正在研究的蛋白质组。为了缓解这一瓶颈，我们建议获得专门为加速LC/MS蛋白质组数据分析而设计的专用高性能计算(HPC)集群。支持集群的软件已在手中或已提交收购，包括SEQUEST、X！Tandem、Mascot和Scaffold。拟议的网格计算系统将设在UB计算研究中心(CCR)，这是一个已建立的HPC研究中心，位于LC/MS蛋白质组学设施附近。拟议的高性能计算机群集中将整合足够的海量存储和后备设施。在数据采集地点战略性放置的工作站将为调查人员提供进入高性能计算机系统的分散通道。蛋白质组学软件的基准测试表明，通过收购拟议的系统，可以实现相当大的MS数据分析速度，该系统利用了四核处理器技术的进步，并利用了非常强大的现有CCR基础设施。鉴于我们的研究人员在基础和临床科学方面的重点，拟议中的设备将对威胁生命的疾病的新疗法的开发以及我们对基本生化和生理过程的理解产生直接、重大的影响。公共卫生相关性：在蛋白质组范围内分析蛋白质表达和翻译后修饰(PTM)是一项重要的新兴技术，将有助于我们理解基本的生化过程、细胞反应网络以及疾病过程对它们的影响。这些信息还有助于开发新的、机械靶向的药物。目前和正在进行的NIH支持的研究项目受到蛋白质组学研究中遇到的大数据集和/或复杂PTM的计算分析瓶颈的阻碍。建议的网格支持的高性能计算集群和分布式处理网络将有效地解决我们区域蛋白质组研究联盟的这一计算瓶颈，从而直接推动NIH支持的旨在改善艾滋病、心血管疾病和癌症等严重疾病治疗的众多项目。

DESCRIPTION (provided by applicant): The University at Buffalo (UB) and regional partners Roswell Park Cancer Inst. (RPCI) and Hauptman-Wood- ward Research Inst. (HWI) are joined in a major continuing investment in infrastructure to support proteomic research directed toward basic, applied, and clinical biomedical applications. A distributed regional network of instrumentation provides access to state-of-the-art liquid chromatography/mass spectrometry (LC/MS) instrumentation. Key instruments generating proteomics data for our consortium of investigators include a Waters LC-QTOF Premier (RPCI), a Thermo LTQ-XL linear ion trap equipped with Electron Transfer Dissociation and multidimensional nano-flow LC (UB Pharmaceutical Sciences Instrumentation Core), and a Thermo LTQ Orbitrap with ion chromatography fractionation and nano-flow LC in UB's new NY State Center of Excellence in Bioinformatics & Life Sciences (CBLS), located physically contiguous to the RPCI campus. Additional LC/MS instruments support the ongoing proteomics research and provide enhanced quantitative capabilities. These include four LC/triple-quadrupole MS (2 API3000 and two Thermo Quantum Ultra EMR) in the UB Pharmaceutical Sciences Core, an API3000 and Thermo Quantum Ultra at RPCI. For this multi-institutional consortium of NIH-supported laboratories, computational analysis of the large proteomics data sets acquired from basic and clinical research samples presents a severe bottleneck. Because of the nature of the research, analyses of experimental data using SEQUEST can require hours to days of computation on standard high-end workstations, depending on the matrix complexity and peptide modifications expected, the use of reagents such as ICAT or iTRAQ to quantify relative expression, and the proteome under study. To alleviate this bottleneck, we propose to acquire a dedicated high-performance computing (HPC) cluster specifically designed to accelerate LC/MS proteomic data analysis. Cluster-enabled software is in hand or committed for acquisition, and includes SEQUEST, X!Tandem, Mascot, and Scaffold. The proposed grid- enabled computing system will be housed in the UB Center for Computational Research (CCR), an established HPC research center located in close proximity to the LC/MS proteomics facilities. Adequate mass storage and backup facilities will be integrated into the proposed HPC cluster. Workstations placed strategically at the sites of data acquisition will provide decentralized access to the HPC system for investigators. Benchmarking of the proteomics software reveals that a considerable acceleration of MS data analysis can be achieved by acquisition of the proposed system, which exploits advances in quad-core processor technology and leverages very substantial existing CCR infrastructure. Given the emphasis of our investigators in both basic and clinical sciences, the proposed equipment will have direct, major impact on the development of new therapies for life-threatening diseases, as well as on our understanding of basic biochemical and physiological processes. PUBLIC HEALTH RELEVANCE: The analysis of protein expression and post-translational modifications (PTMs) on a proteome-wide scale represents an important emerging technique that will assist in our understanding of fundamental biochemical processes, networks of cellular responses, and the impact of disease processes upon them. This information can also contribute to the development of new, mechanistically-targeted drugs. Current and pending NIH-supported research projects are hampered by bottlenecks in the computational analysis of the large data sets and/or complex PTMs that are encountered in proteomics research. The proposed grid-enable high-performance computing cluster and distributed processing network will effectively address this computational bottleneck for our regional proteomics research consortium, and thereby directly advance the numerous NIH- supported projects aimed at improving the therapy of serious diseases such as AIDS, cardiovascular disease, and cancer.