CDI-Type II: MS-Omics Hub for Cyber-enabled Acceleration of Mass Spectrometry-based Metabolomics and Proteomics

CDI-Type II：MS-Omics 中心，用于网络加速基于质谱的代谢组学和蛋白质组学

• 批准号: 0941143
• 负责人: Christodoulos Floudas
• 金额: $ 130.32万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941143&HistoricalAwards=false
• 关键词: CDI; Type; II; MS; Omics

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Cyber-Enabled Discovery and Innovation (CDI)Proposal Number: 0941143P/I: Christodoulos FloudasInstitution: Princeton UniversityTitle: CDI-Type II: MS-Omics Hub for Cyber-enabled Acceleration of Mass Spectrometry-based Metabolomics and ProteomicsScope of Project and Intellectual Merit:Mass spectrometry technology has the potential to revolutionize the biological sciences by enabling quantitative and comprehensive assessment of proteins (proteomics) and metabolites (metabolomics). A major challenge, however, is converting raw mass spectrometry data into information useful to biologists. This project represents an effort to transform proteomics and metabolomics by providing an open-source platform for analysis of mass spectrometry-based metabolomics and proteomics data which capitalizes on cyberinfrastructure to accelerate biological discovery. Specifically, the project will develop a unified platform for identification and quantitation of metabolites, peptides, and proteins (the MS-Omics Hub) that (1) allows the user to enter mass spectrometry data from diverse instrumentation and returns to the user identities and quantities of assignable metabolite, peptide, and protein peaks, (2) highlights novel, biologically significant species whose importance emerged through cyber-enabled integration of data from diverse users, and (3) provides enhanced computational algorithms for identification of covalently-modified proteins. These algorithms will revolve around an integer linear optimization framework that the PIs have recently shown can substantially improve proteomic data analysis. The utility of the MS-Omics Hub will be demonstrated through its application to a broad spectrum of data, generated both by the grant team and by a diversity of scientists nationwide. Intellectual Merit: This effort tackles a barrier preventing proteomics and metabolomics from broadly impacting biological research: the difficulty of extracting compound identities and quantities from raw data. It furthermore addresses the intellectually challenging aspect of proteomic data analysis: computational identification of peaks arising from covalently modified proteins. Finally, it applies cyber-infrastructure to accelerate the identification of peaks arising from novel, biologically-significant metabolites and protein covalent modification sites. The diversity of intellectual challenges involved is mirrored by the multidisciplinary nature of the research team: Floudas, Garcia, and Rabinowitz come from three different departments at Princeton University (Chemical Engineering, Molecular Biology, and Chemistry, respectively), and bring expertise spanning global optimization, scientific computing, mathematical modeling, proteomics, metabolomics, and analytical chemistry. They are unified by their interest in comprehensive, quantitative, computationally-enabled analysis of biochemical systems. Broader Impacts:This research has the potential to transform the biological sciences, by accelerating progress in proteomics and metabolomics and by rendering the power of these emerging fields accessible to a broad spectrum of scientists nationwide. Impact on Society: By enabling a larger research community to conduct state-of-the-art metabolomic and proteomic data analysis, the MS-Omics Hub will expedite global research efforts. By providing an archive of biologically significant peaks, it will accelerate discovery of novel metabolites and protein covalent modification sites. These will likely include novel biomarkers and bioactive compounds. Areas in which improved metabolomic and proteomic capabilities will be applied include medicine, drug discovery, agriculture, bioenergy, and environmental science. Integration of Research and Education: This effort will integrate participation of undergraduate and graduate students in all aspects of the research program, include underrepresented minorities and visiting students from small colleges. The students will receive training in mass spectrometry, computational biology, systems biology, and scientific computation. In addition, as an open-source platform, the MS-Omics Hub will be available as an educational tool to scholars nationwide. The PIs also plan to host an annual ?users? conference to interact with and train the outside MS-Omics Hub users, and to recruit new users to the Hub. Broaden Representation of Underrepresented Groups: Each of the PIs will actively recruit minority students for summer research. Dissemination: In addition to dissemination through standard channels (journals, refereed proceedings, conferences), the MS-Omics Hub will be introduced to the community through a network of mass spectrometry experts, biological beta-testers, and the summer conference. The MS-Omics Hub itself will be open-source (code available on web) and freely accessible to scientists worldwide.

该奖项是根据2009年的《美国复苏与重新申请法》（公法111-5）资助的。基于CYBER启用的发现与创新（CDI）提案编号：0941143P/I：Christodoulos Floudasinstitution：Princeteton Universitytitle：CDI-Type II：MS--型：MS--型：MS-------------- OMICS基于质谱的代谢组学加速的网络枢纽项目和智力优点的蛋白质组学：质谱技术具有通过实现蛋白质（蛋白质组学）和代谢物（代谢物）（代谢物）（代谢物）的定量和全面评估的生物学科学的潜力。但是，一个主要的挑战是将原始质谱数据转换为对生物学家有用的信息。该项目代表了通过提供一个开源平台来转化蛋白质组学和代谢组学的努力，用于分析基于质谱的代谢组学和蛋白质组学数据，该数据利用了网络基础结构来加速生物学发现。具体而言，该项目将开发一个统一的平台，用于识别和定量代谢物，肽和蛋白质（MS-OMICS HUB）（1）允许用户从各种仪器中输入质谱数据，并返回用户身份和数量（2）突出显示新颖的物种，其重要性通过来自不同用户的数据积分而出现的重要性，（3）提供了增强的计算算法，以鉴定共同改性的蛋白质。这些算法将围绕PIS最近显示的整数线性优化框架围绕该算法，可以实质上改善蛋白质组学数据分析。 MS-OMIC HUB的实用性将通过其应用于赠款团队和全国各种科学家生成的广泛数据的应用来证明。知识分子的优点：这项工作解决了阻止蛋白质组学和代谢组学广泛影响生物学研究的障碍：从原始数据中提取复合身份和数量的困难。此外，它解决了蛋白质组学数据分析的智力挑战方面：由共价修改的蛋白质引起的峰的计算识别。最后，它应用网络基础结构来加速鉴定由新型，生物学上重要的代谢产物和蛋白质共价修饰位点引起的峰。涉及的智力挑战的多样性反映了研究团队的跨学科性质：Floudas，Garcia和Rabinowitz分别来自普林斯顿大学的三个不同系（分别化学工程，分子生物学和化学），并带来跨越全球优化的专业知识，科学计算，数学建模，蛋白质组学，代谢组学和分析化学。他们对生物化学系统的全面，定量，计算的分析的兴趣统一。更广泛的影响：这项研究有可能通过加速蛋白质组学和代谢组学的进步以及通过使全国各种科学家可以访问这些新兴领域的力量来改变生物科学。对社会的影响：通过使更大的研究社区能够进行最先进的代谢组和蛋白质组学数据分析，MS-MOMICS HUB将加快全球研究工作。通过提供具有生物学上显着峰的档案，它将加速发现新型代谢产物和蛋白质共价修饰位点。这些可能包括新型的生物标志物和生物活性化合物。改善代谢组和蛋白质组学能力的领域包括医学，药物发现，农业，生物能源和环境科学。研究和教育的整合：这项工作将整合研究计划各个方面的本科生和研究生的参与，包括代表性不足的少数群体和访问小学的学生。学生将接受质谱，计算生物学，系统生物学和科学计算的培训。此外，作为一个开源平台，MS-MOMICS中心将作为全国学者的教育工具提供。 PI还计划托管年度用户？会议与外部MS-OMIC HUB用户进行互动和培训，并将新用户招募到集线器。扩大代表性不足的群体的代表性：每个PI都会积极招募少数民族学生进行夏季研究。传播：除了通过标准渠道传播（期刊，审查程序，会议）外，MS-MOMICS HUB还将通过质谱专家，生物β-测试者和夏季会议的网络介绍社区。 MS-MOMIC HUB本身将是开源（网络上的代码），并且在全球科学家可以自由访问。