ABI Innovation: A Metabolomics Toolkit for NMR and Mass Spectrometry

在生物基础设施部生物信息学进展的支持下，内布拉斯加州大学林肯分校的罗伯特·鲍尔斯教授和他的研究小组将进一步开发他们的MVAPACK软件工具包，分析代谢组学研究中产生的数据。代谢组学研究的是生物过程的产物，如糖等小分子的化学特征。代谢物指纹揭示了生物样品中最近发生的许多活动。了解生物对周围环境的反应对理解植物生物学和农业、环境和水质研究以及人类健康营养或疾病研究等领域的事件具有重要意义。化学指纹是使用两种不同的技术产生的，称为核磁共振和质谱分析。结果可能非常复杂，很难合并。这意味着数据分析的挑战也很大，包括开发新方法的要求，比如多元统计方法。这项提议旨在制作软件工具，从数据中提取最可能的信息，包括数据格式转换、预处理、验证、重要性测试、可视化以及在确定的置信度水平下的代谢物识别。MVAPACK工具包将以免费提供和开源软件模块的形式实现重要的、目前无法获得的数据处理功能，对数据质量、共享、信息内容产生影响，从而从包括代谢物分析在内的研究中获得更深层次的生物学见解。在获奖期间，该研究项目将通过包括额外和迫切需要的功能来进一步增强MVAPACK代谢组学工具包。这些改进将包括增加加载和处理液相色谱(LC)-MS、气相色谱(GC)-MS和多维数据集的功能；扩展为化学计量学集成多个数据集的能力；以及自动识别代谢物，这是代谢组学更具挑战性和耗时的方面之一。这项工作将使用图论、协方差核磁共振、反向比例伪谱加载以及核磁共振和MS相结合的方法来促进复杂生物样品中代谢物的准确和快速分配。总体而言，这些努力将导致为代谢组学科学界改进MVAPACK软件包(http://bionmr.unl.edu/mvapack.php))，该软件包将增强信息含量、实用性以及处理和组合各种类型的核磁共振和MS数据集的简便性。

With support from the Advances in Biological Informatics in the Division of Biological Infrastructure, Professor Robert Powers and his research group at the University of Nebraska - Lincoln will further develop their MVAPACK software toolkit that analyzes data generated in metabolomics research. Metabolomics is the study of the chemical signatures of the small molecules, like sugars, that are the products of biological processes. Metabolite fingerprints reveal a great deal about the recent activities occurring in biological samples. Knowing how organisms respond to their surroundings gives important leads to understanding events in fields like plant biology and agriculture, environmental and water quality studies, and human health research into nutrition or diseases. The chemical fingerprints are generated using two different technologies, called nuclear magnetic resonance and mass spectrometry. The results can be very complex and difficult to merge. This means the challenges for data analysis are also high, and include requirements to develop new approaches, like multivariate statistical methods. This proposal aims to produce software tools that will extract the most possible information from the data, including data format conversion, preprocessing, validation, significance testing, visualization, and metabolite identification at defined levels of confidence. The MVAPACK toolkit will enable important, currently unavailable data processing functions in the form of freely available and open source software modules, with an impact on data quality, sharing, information content resulting in deeper biological insights from studies that include metabolite profiling. During the award period, this research project will further enhance the MVAPACK metabolomics toolkit by including additional and urgently needed capabilities. Such improvements will include adding features to load and process liquid chromatography (LC)-MS, gas chromatography(GC)-MS and multidimensional datasets; to expand capabilities to integrate multiple datasets for chemometrics; and to automate metabolite identification, which is one of the more challenging and time-consuming aspects of metabolomics. This work will employ, among other approaches, graph theory, covariance NMR, back-scaled pseudospectral loadings, and a combined NMR and MS approach to facilitate the accurate and rapid assignment of metabolites from complex biological samples. Overall, these efforts will lead to an improved MVAPACK software package (http://bionmr.unl.edu/mvapack.php) for the metabolomics scientific community that will enhance the information content, utility, and ease of processing and combining various types of NMR and MS datasets.