High Performance Computational System to Support LCMS/Proteomics Analysis

描述（由申请人提供）：布法罗大学（UB）和区域合作伙伴罗斯威尔公园癌症研究所（RPCI）和Hauptman-Wood- ward研究所（HWI）加入了一项重大的基础设施持续投资，以支持针对基础，应用和临床生物医学应用的蛋白质组学研究。分布式区域仪器网络提供访问最先进的液相色谱/质谱（LC/MS）仪器。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)， Thermo LTQ- xl线性离子阱，配备电子转移解离和多维纳米流LC （UB制药科学仪器核心），以及位于UB新成立的纽约州生物信息学与生命科学卓越中心（CBLS）的离子色谱分离和纳米流LC的Thermo LTQ Orbitrap。该中心位于RPCI校园附近。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。其中包括UB Pharmaceutical Sciences Core的四个LC/三重四极杆质谱（2个API3000和2个Thermo Quantum Ultra EMR）， RPCI的API3000和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，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出现了严重的瓶颈。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进行数小时到数天的计算，这取决于预期的基质复杂性和肽修饰，使用试剂如ICAT或iTRAQ来量化相对表达，以及所研究的蛋白质组。为了缓解这一瓶颈，我们建议购买专用的高性能计算（HPC）集群，专门设计用于加速LC/MS蛋白质组学数据分析。支持集群的软件已到手或已提交收购，包括SEQUEST、X！串联，吉祥物和脚手架。拟议的网格计算系统将被安置在UB计算研究中心（CCR），这是一个建立的高性能计算研究中心，位于LC/MS蛋白质组学设施附近。拟议的高性能计算集群将整合足够的大容量存储和备份设施。战略性地放置在数据采集地点的工作站将为调查人员提供对HPC系统的分散访问。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MS数据分析的显著加速，该系统利用了四核处理器技术的进步，并利用了非常大量的现有CCR基础设施。鉴于我们的研究人员在基础科学和临床科学方面的重点，拟议的设备将对危及生命的疾病的新疗法的发展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（PTMs）是一项重要的新兴技术，将有助于我们理解基本生化过程、细胞反应网络以及疾病过程对它们的影响。这些信息也有助于开发新的机械靶向药物。在蛋白质组学研究中遇到的大型数据集和/或复杂的ptm的计算分析瓶颈阻碍了当前和未决的nih支持的研究项目。提出的网格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学研究联盟的计算瓶颈，从而直接推进NIH支持的众多旨在改善艾滋病、心血管疾病和癌症等严重疾病治疗的项目。

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.