SBIR Phase II: Sample Classification and Biomarker Discovery by Comprehensive Metabolomic Analysis

这个小型企业创新研究第二阶段项目建议开发一个系统，用于生物样本的自动分类和生物标志物的发现。 该系统将被设计用于对综合二维色谱（GCxGC）和高分辨率质谱（HRMS）产生的最先进的生化分离进行综合模式分析。 GCxGC和HRMS的配对将高效的分子分离与精确的元素分析相结合。 有效利用GCxGC-HRMS进行生化样品分类和生物标志物发现的关键挑战是难以分析和解释代谢组学特征的大量复杂数据。 数据的数量和复杂性，以及代谢组的大维度，以及重要的化学特征可能是微妙的，并涉及多种成分的模式的可能性，需要调查和新的生物信息学的发展。 主要技术目标是一个创新框架，用于对许多样本进行全面的特征匹配和分析。 具体而言，该框架将结合用于多维峰检测、跨大样本集的峰模式匹配、数据对齐、综合特征匹配和多样本分析（例如，分类和生物标志物发现）。 预期的成果是一个用于自动化多样品分析的商业系统，通过改进生物分类和生物标志物发现的信息学，将实现该项目更广泛的影响/商业潜力。 这些工具将使研究人员能够更好地了解生物化学过程并发现代谢生物标志物，从而改进疾病诊断和治疗方法。 这些信息技术将促进利用先进的GCxGC-HRMS仪器，从而推动未来的仪器开发。 本项目开发的信息学也将与涉及多维、多光谱数据的其他分类问题有关，包括其他应用（如生物燃料）、其他类型的化学分析（如多维光谱学）和其他领域（如遥感多光谱地理空间成像仪）。 该项目将有助于提高国家在全球分析技术市场上的竞争力，并将通过实习和学生项目让学生参与研究经验，促进劳动力发展。 该项目开发的软件和一个示例数据集将提供给教育机构，让学生更容易地探索生物化学的复杂性。

This Small Business Innovation Research Phase II project proposes to develop a system for automated classification of biological samples and discovery of biomarkers. The system will be designed to perform comprehensive pattern analysis of state-of-the-art biochemical separations generated by comprehensive two-dimensional chromatography (GCxGC) with high-resolution mass spectrometry (HRMS). The pairing of GCxGC and HRMS combines highly effective molecular separations with precise elemental analysis. A critical challenge for effective utilization of GCxGC-HRMS for biochemical sample classification and biomarker discovery is the difficulty of analyzing and interpreting the massive, complex data for metabolomic features. The quantity and complexity of the data, as well as the large dimensionality of the metabolome, and the possibility that significant chemical characteristics may be subtle and involve patterns of multiple constituents, necessitate investigation and development of new bioinformatics. The principal technical objective is an innovative framework for comprehensive feature matching and analysis across many samples. Specifically, the framework will incorporate advanced methods for multidimensional peak detection, peak pattern matching across large sample sets, data alignment, comprehensive feature matching, and multi-sample analyses (e.g., classification and biomarker discovery) with large sample sets. The anticipated result is a commercial system for automated multi-sample analysis.The broader impact/commercial potential of this project will be realized through improved informatics for biological classification and biomarker discovery. These tools will enable researchers to better understand biochemical processes and to discover metabolic biomarkers, which could lead to improved methods for disease diagnoses and treatments. These information technologies will foster utilization of advanced GCxGC-HRMS instrumentation, thereby contributing to the impetus for future instrument development. The informatics developed in this project also will be relevant for other classification problems involving multidimensional, multispectral data, including other applications (such as biofuels), other types of chemical analyses (such as multidimensional spectroscopy), and other fields (such as remote-sensing multispectral geospatial imagers). This project will contribute to national competitiveness in the global market for analytical technologies and will contribute to workforce development by involving students in research experiences through internships and student projects. Software developed in the project and an example dataset will be available to educational institutions to allow students to more easily explore biochemical complexity.