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年美国复苏和再投资法案（公法111-5）资助的。网络发现与创新（CDI）提案编号：0941143P/I: Christodoulos FloudasInstitution: Princeton university标题：CDI- type - II: MS-Omics网络加速基于质谱的代谢组学和蛋白质组学项目范围和知识价值：质谱技术有可能通过对蛋白质（蛋白质组学）和代谢物（代谢组学）进行定量和全面评估，从而彻底改变生物科学。然而，一个主要的挑战是将原始质谱数据转化为对生物学家有用的信息。该项目为基于质谱的代谢组学和蛋白质组学数据分析提供了一个开源平台，利用网络基础设施加速生物学发现，从而代表了蛋白质组学和代谢组学转型的努力。具体来说，该项目将开发一个统一的平台，用于代谢物、肽和蛋白质的鉴定和定量（MS-Omics Hub），该平台(1)允许用户输入来自不同仪器的质谱数据，并返回可分配代谢物、肽和蛋白质峰的用户身份和数量；(2)突出新的、具有生物学意义的物种，其重要性通过网络集成来自不同用户的数据而显现出来。(3)提供了用于共价修饰蛋白鉴定的增强计算算法。这些算法将围绕一个整数线性优化框架，pi最近显示可以大大提高蛋白质组学数据分析。MS-Omics Hub的效用将通过其对广泛数据的应用得到证明，这些数据由资助团队和全国各地的各种科学家生成。知识价值：这一努力解决了阻碍蛋白质组学和代谢组学广泛影响生物学研究的障碍：从原始数据中提取化合物身份和数量的困难。它进一步解决了蛋白质组学数据分析的智力挑战方面：由共价修饰蛋白质产生的峰的计算识别。最后，它应用网络基础设施来加速识别由新的、具有生物学意义的代谢物和蛋白质共价修饰位点产生的峰。研究团队的多学科性质反映了所涉及的智力挑战的多样性：Floudas， Garcia和Rabinowitz来自普林斯顿大学三个不同的部门（分别是化学工程，分子生物学和化学），并带来了跨越全球优化，科学计算，数学建模，蛋白质组学，代谢组学和分析化学的专业知识。他们在生化系统的全面，定量，计算能力分析的兴趣统一。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展，并通过使这些新兴领域的力量为全国范围内广泛的科学家所利用，来改变生物科学。对社会的影响：通过使更大的研究团体能够进行最先进的代谢组学和蛋白质组学数据分析，MS-Omics Hub将加快全球研究工作。通过提供具有生物学意义的峰值档案，它将加速发现新的代谢物和蛋白质共价修饰位点。这些可能包括新的生物标志物和生物活性化合物。改进的代谢组学和蛋白质组学能力将应用的领域包括医学、药物发现、农业、生物能源和环境科学。研究与教育的整合：这项工作将整合本科生和研究生参与研究项目的各个方面，包括代表性不足的少数民族和来自小型学院的访问学生。学生将接受质谱、计算生物学、系统生物学和科学计算方面的培训。此外，MS-Omics Hub将作为开放资源平台，向全国学者提供教育工具。pi还计划举办一年一度的用户研讨会。会议与外部MS-Omics Hub用户进行互动和培训，并为Hub招募新用户。扩大代表性不足的群体：每个pi将积极招募少数民族学生进行暑期研究。传播：除了通过标准渠道（期刊、评审论文集、会议）传播外，MS-Omics Hub还将通过质谱专家网络、生物beta测试者网络和夏季会议向社区介绍。ms组学中心本身将是开源的（代码可以在网上找到），全世界的科学家都可以免费使用。