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为分析疾病标志物的复杂生物液体提供了重要的新技术能力。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（GCxGC/TOF-MS），它提供了高精度和扩展的分子检测能力。然而，该平台的数据分析并不适用于高样本数或检测该技术检测的大量分析物（每个生物流体样品超过1500个）。我们将通过以下具体目标开发一个信息学平台，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，(3)信息学分析工具集成。我们的系统将传递的生物学信息是改变代谢物的调节，这些代谢物与样品组之间的表型差异有关。这些信息将有助于评估人类健康和福利，并将对公共卫生产生直接影响。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血浆样本的代谢组学分析数据，并与对照组进行比较。总的来说，我们的信息学工具将促进代谢组学社区在强大的GCXGC-TOF-MS平台上进行高精度和高容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准确性的算法，光谱校准和数据集规范化。将开发单独的算法来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显著性背景。将开发另一组算法，以实现代谢物相关网络的交互式可视化分析。该项目的交付成果将包括提供可通过web服务远程调用的独立软件模块，以及用于GCxGC/TOF-MS数据的基于web的集成数据分析管道。公共卫生相关性：高通量分子组学分析平台为分析疾病标志物的复杂生物流体提供了重要的新技术能力。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（GCxGC/TOF-MS），它提供了高精度和扩展的分子检测能力。这种方法通常在主分析柱之后使用短极性柱。通常，第二柱在比第一柱更高的温度下操作。由于柱温和色谱基质的差异，从第一色谱柱共洗脱的代谢物在第二色谱柱进一步分离。进一步分离的代谢物被定向到高容量飞行时间质谱系统进行检测。二维气相色谱法对复杂样品的分析具有显著的优势，包括：分离能力的数量级提高，信噪比和动态范围的显著提高，质谱反卷积和相似性匹配的改进。由于GCWGC/TOF-MS能提供更多准确的信息，是分析复杂生物系统中小分子代谢物的有力工具。然而，该平台的数据分析并不适用于高样本数或检测该技术检测的大量分析物（每个生物流体样品超过1500个）。我们将通过以下具体目标开发一套计算算法，以准确有效地分析GCxGC/TOF-MS分析产生的数据：(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，(3)信息学分析工具集成。我们的系统将传递的生物学信息是改变代谢物的调节，这些代谢物与样品组之间的表型差异有关。这些信息将有助于评估人类健康和福利，并将对公共卫生产生直接影响。这类信息的一个直接应用是在临床生物医学研究中发现代谢物生物标志物。在现有合作的背景下，我们开发的大规模信息学系统的初始大规模应用重点将是分析乳腺癌和多发性骨髓瘤患者血浆样本的代谢组学分析数据，并与相应的对照受试者进行比较。以原始仪器数据作为输入信息，我们的信息学工具将执行一系列数据挖掘，发现样品中存在的代谢物，并提供其调节信息。这些信息学工具将促进代谢组学社区在强大的GCXGC-TOF-MS平台上进行高精度和高容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准确性的算法，光谱校准和数据集规范化。将开发单独的算法来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显著性背景。将开发另一组算法，以实现代谢物相关网络的交互式可视化分析。该项目的交付成果将包括提供可通过web服务远程调用的独立软件模块，以及用于GCxGC/TOF-MS数据的基于web的集成数据分析管道。

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. PUBLIC HEALTH RELEVANCE: High-throughput molecular 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. This approach usually uses a short polar column after the main analytical column. Typically, the second column is operated at a higher temperature than the first column. The metabolites co-eluted from the first GC column are further separated in the second column because of the difference of column temperature and the chromatography matrix. The further separated metabolites are directed to a high capacity time-of-flight mass spectrometry system for detection. Two- dimensional gas chromatography offers significant advantages for analysis of complex samples including: an order-of-magnitude increase in separation capacity, significant increase in signal-to-noise ratio and dynamic range, and improvement of mass spectral deconvolution and similarity matches. Since GCWGC/TOF-MS can provide more and accurate information, it represents powerful tool for the analysis of small molecule metabolites in complex biological systems. 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 a set of computational algorithms 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. A direct application of this type of information is metabolite biomarker discovery in clinical biomedical research. In the context of existing collaborations, our initial large scale application focus of the developed mass informatics system will be analyses of metabolomic profiling data from human plasma samples of patients with breast cancer and human multiple myeloma, compared with corresponding control subjects. With the raw instrument data as input information, our informatics tools will perform a series of data mining and discover the metabolites present in the sample and also provide their regulation information. These 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.