Mass Informatics of Two Dimensinoal Gas Chromatography Time-of-flight Mass Spectr

二维气相色谱飞行时间质谱的质量信息学

• 批准号: 8070046
• 负责人: Xiang Zhang
• 金额: $ 35.14万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8070046
• 关键词: Algorithms; Area; Biological; Biological Markers; Biomedical Research; Blood; Chromatography; Clinical; Collaborations; Communities; Complex; Complex Mixtures; Computational algorithm; Computer software; Data; Data Analyses; Data Files; Data Set; Detection; Development; Disease; Disease Marker; Emerging Technologies; Equilibrium; Gas Chromatography; Health; High temperature of physical object; Human; Informatics; Investigation; Liquid substance; Malignant Neoplasms; Malignant neoplasm of prostate; Marketing; Mass Fragmentography; Mass Spectrum Analysis; Measures; Medicine; Metabolic; Methods; Mission; Modeling; Modification; Molecular; Monitor; Multiple Myeloma; Noise; Online Systems; Output; Pathway Analysis; Patients; Pattern Recognition; Plasma; Preventive; Probability; Process; Public Health; Regulation; Relative (related person); Research Personnel; Residual state; Running; Sampling; Secure; Series; Signal Transduction; Simulate; Solutions; Source; Speed; Statistical Models; System; Technology; Temperature; Testing; Time; Urine; Variant; Visual; Work; base; clinically relevant; cohort; comparative; complex biological systems; data mining; direct application; graphical user interface; human data; improved; instrument; instrumentation; interest; liquid chromatography mass spectrometry; malignant breast neoplasm; metabolomics; open source; programs; small molecule; software systems; tool; two-dimensional; web services

DESCRIPTION (provided by applicant): High-throughput omics analysis platforms provide important new technical capabilities to analyze complex biological fluids for disease markers. A particularly important platform for metabolomics is two-dimensional gas chromatography time-of- flight mass spectrometry (GCxGC/TOF-MS) that provides both high precision and extended capacity for molecular detection. However, data analysis from this platform is not developed for high sample numbers or for detection of the very large numbers of analytes that this technology detects (more than 1500 per biofluid sample). We will develop an informatics platform for accurate and efficient analysis of data generated from GCxGC/TOF-MS analyses through the following specific aims: (1) Mass informatics for differential metabolomics, (2) Interactive visual analysis of metabolite correlation networks, and (3) Informatics analysis tools integration. The biological information that our system will deliver is regulation of altered metabolites that relate to phenotypic differences between groups of samples. This information will enable assessment of human health and wellness, and will have a direct impact on public health. In the context of existing collaborations, our initial large scale application focus will be analyses of metabolomic profiling data from plasma samples of patients with breast and prostate cancer compared with control subjects. Broadly speaking, our informatics tools will facilitate efforts in the metabolomics community to perform comparative metabolite profiling with high precision and high volume on the powerful GCXGC-TOF-MS platform. The project consists of the development of algorithms for assessment of metabolite identification accuracy, alignment of spectra, and normalization of datasets. Separate algorithms will be developed to distinguish specific molecules that are observed to be different between different sample cohorts and to provide a statistical significance context for each differentially expressed molecule. An additional group of algorithms will be developed to enable interactive visual analysis of metabolite correlation networks. Deliverables from this project will include provision of independent software modules that can be remotely invoked via web services and an integrated web-based data analysis pipeline for GCxGC/TOF-MS data.

描述（由申请人提供）：高通量的OMICS分析平台提供了重要的新技术能力，可以分析用于疾病标志物的复杂生物学流体。代谢组学的一个特别重要的平台是二维气相色谱时间 - 飞行质谱时间（GCXGC/TOF-MS），可提供高精度和扩展能力的分子检测能力。但是，该平台的数据分析尚未用于高样本数量或检测该技术检测到的大量分析物（每个生物流体样品超过1500）。我们将开发一个信息平台，以通过以下特定目的对从GCXGC/TOF-MS分析产生的数据进行准确有效分析：（1）差异代谢组学的质量信息学，（2）代谢物相关网络的交互式视觉分析，以及（3）信息学分析工具工具集成。我们系统将提供的生物学信息是调节与样品组之间的表型差异有关的改变的代谢产物。这些信息将对人类健康和保健进行评估，并将直接影响公共卫生。在现有合作的背景下，与对照组相比，我们最初的大规模应用将重点是分析乳腺癌和前列腺癌患者血浆样本的代谢组分析数据。从广义上讲，我们的信息学工具将促进代谢组学社区的努力，以在功能强大的GCXGC-TOF-MS平台上进行高精度和大量的比较代谢物分析。该项目包括开发用于评估代谢物鉴定精度，光谱对齐和数据集归一化的算法。将开发单独的算法，以区分不同样品队列之间观察到的特定分子，并为每个差异表达的分子提供统计显着性上下文。将开发另一组算法，以实现代谢物相关网络的交互式视觉分析。该项目的可交付成果将包括提供独立的软件模块，这些模块可以通过Web服务远程调用，以及用于GCXGC/TOF-MS数据的基于Web的集成数据分析管道。