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

这个小企业创新研究（SBIR）第一阶段项目建议开发一个生物样本自动分类和生物标记物发现系统。目标是建立一个系统，对由综合二维气相色谱（GCxGC）和高分辨率质谱（HRMS）产生的最先进的生化分离进行全面的模式分析。选择性利用GCxGC-HRMS进行生化分类和生物标志物发现的一个关键挑战是难以分析和解释代谢组学和蛋白质组学特征的大量复杂数据。数据的数量和复杂性，以及生物化学的大维度，其中重要的特征可能是微妙的，涉及多种成分的变化模式，需要研究和发展新的生物信息学。主要的技术目标是一个创新的框架，用于跨许多样本进行全面的特征匹配和分析。特征匹配是统一标记结构的基础，这样可以记录相似性和差异性。具体来说，该框架将结合多维峰检测、跨大样本集的峰模式匹配、数据对齐、GCxGC-HRMS特征计算和大型特征集分类的先进方法。预期的结果是商业系统分类生物样本和识别重要生物标志物的技术基础。如果成功，该项目的更广泛影响/商业潜力将是更好地了解生化过程和发现代谢组学和蛋白质组学生物标志物，从而改进疾病诊断和治疗方法。这些创新的生物信息学将有助于提高全球分析技术市场的经济竞争力，并将促进先进的GCxGC-HRMS仪器的使用。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相关，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）和其他领域（如遥感多光谱地理空间成像仪）。该项目将通过实习和项目赞助让学生实习生参与研究经验，从而促进劳动力发展；通过提供软件和示例数据，让学生更容易地探索生物化学的复杂性，从而促进教育。

This Small Business Innovation Research (SBIR) Phase I project proposes to develop a system for automated classification of biological samples and discovery of biomarkers. The goal is a system to perform comprehensive pattern analysis of state-of-the-art biochemical separations generated by comprehensive two-dimensional gas chromatography (GCxGC) with high-resolution mass spectrometry (HRMS). A critical challenge for elective utilization of GCxGC-HRMS for biochemical classification and biomarker discovery is the diffculty of analyzing and interpreting the massive, complex data for metabolomic and proteomic features. The quantity and complexity of the data, as well as the large dimensionality of the biochemistry in which significant characteristics may be subtle and involve patterns of variations in multiple constituents, necessitate the investigation and development of new bioinformatics. The principal technical objective is an innovative framework for comprehensive feature matching and analysis across many samples. Feature matching is the basis for uniformly labeling structures so that similarities and differences can be documented. Specifically, the framework will incorporate advanced methods for multidimensional peak detection, peak pattern matching across large sample sets, data alignment, GCxGC-HRMS feature computations, and classification with large feature sets. The anticipated result is the technical foundation for a commercial system to classify biological samples and identify significant biomarkers.The broader impact/commercial potential of this project, if successful, will be a better understanding of biochemical processes and discovery of metabolomic and proteomic biomarkers, leading to improved methods for disease diagnoses and treatments. These innovative bioinformatics will contribute to economic competitiveness in the global market for analytical technologies and will foster utilization of advanced GCxGC-HRMS instrumentation. 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). The project will contribute to workforce development, by involving student interns in research experiences through internships and project sponsorships, and to education, by providing software and example data to allow students to more easily explore biochemical complexity.