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

描述(由申请人提供)：高通量组学分析平台提供了重要的新技术能力，用于分析复杂的生物体液以寻找疾病标记物。代谢组学的一个特别重要的平台是二维气相色谱飞行时间质谱仪(GCxGC/TOF-MS)，它为分子检测提供了高精度和扩展能力。然而，该平台的数据分析并不是针对高样本数量或该技术检测到的大量分析物(每个生物流体样本超过1500个)而开发的。我们将开发一个信息平台，通过以下具体目标对GCxGC/TOF-MS分析产生的数据进行准确和高效的分析：(1)差异代谢组学的海量信息学，(2)代谢物相关网络的交互式可视化分析，以及(3)信息学分析工具的集成。我们的系统将提供的生物信息是与样本组之间的表型差异有关的改变代谢物的调节。这些信息将使评估人类健康和健康成为可能，并将对公共健康产生直接影响。在现有合作的背景下，我们最初的大规模应用重点将是分析乳腺癌和前列腺癌患者与对照受试者的血浆样本中的代谢组谱数据。总的来说，我们的信息学工具将促进代谢组学领域的努力，在强大的GCxGC-TOF-MS平台上进行高精度和高容量的代谢物比较分析。该项目包括开发用于评估代谢物鉴定准确性、光谱对齐和数据集归一化的算法。将开发单独的算法，以区分在不同样本队列中观察到的不同的特定分子，并为每个差异表达的分子提供统计意义背景。将开发另外一组算法，以实现代谢物相关网络的交互式视觉分析。该项目的成果将包括提供可通过网络服务远程调用的独立软件模块，以及针对GCxGC/TOF-MS数据的综合网络数据分析管道。

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.