SBIR Phase I: Pipeline for Analysis of Metabolomics Data

小型企业创新研究(SBIR)第一阶段项目的更广泛影响是加强代谢物(细胞用于生长、繁殖和健康的物质)分析在日益增长的系统生物学研究领域的作用。预计这将导致更快、更便宜的生物标记物和药物发现，以实现更准确、可重复性和更快的临床试验，并加快对许多细胞和系统生物体研究领域的基础科学研究。这将对制药公司的底线以及改善健康和降低医疗成本产生重大影响。这项创新将为客户提供一个平台和专业知识，使他们能够通过以下方式更快地提高开发生物标记物和药物的能力：(1)允许更多的代谢物参与发现疾病和代谢物之间的新关系，潜在地开辟基础研究的新领域；(2)不仅根据代谢物水平的统计显著变化，而且根据疾病与健康细胞代谢物之间相互作用的相关性来选择与疾病相关的代谢物；以及(3)通过将代谢物分析与其他系统范围的分析方法(如基因表达、蛋白质水平等)相结合来评估代谢物与疾病之间的关系。拟议的项目旨在开发一个创新的基于云的平台，该平台具有交互式模块界面，允许用户轻松地构建定制管道，用于分析非靶向代谢组学数据。该平台将使人们有机会增加注释分析物的数量，并将代谢组学与其他组学数据相结合，从而加强代谢组学在基于系统生物学的生物标记物和药物发现研究中的参与。尽管在过去的几年中获得了大量的代谢组学数据，但这些数据在生物标记物和药物发现方面的有效利用一直非常有限。这些挑战的部分原因是缺乏有效的工具：(1)准确确定疾病相关分析物的身份；(2)帮助研究疾病进展过程中代谢物之间的重新连接和保守的相互作用；以及(3)整合多组学数据，在系统水平上评估代谢物与疾病之间的关系。该项目将通过调查和评估代谢物注释、代谢物图谱的差异分析和多组学数据集成的创新计算方法来促进科学知识的发展。此外，该项目将建立一个基于云的平台，使用户能够通过从几个创新模块中选择分析代谢组学数据来构建他们所需的数据分析工作流程或管道。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is enhancing the role of metabolite (substances used by cells for growth, reproduction and health) analysis in the growing area of systems biology research. This is expected to lead to faster and less expensive biomarker and drug discovery to allow for more accurate, reproducible, and faster clinical trials, and to accelerate basic scientific research into many areas of cellular and system-wide organismal studies. This will have a significant impact on the bottom line for drug companies and for improving health and reducing health care cost. The innovation will provide customers with a platform and expertise that enable them to increase their ability to develop biomarkers and drugs faster by: (1) allowing more metabolites to be involved in the discovery of new relationships between diseases and metabolites, potentially opening up new areas of basic research; (2) selecting disease-associated metabolites on the basis of not only statistically significant changes in metabolite levels but also correlations of interactions among metabolites in diseased vs. healthy cells; and (3) evaluating the relationships between metabolites and diseases through integration of metabolite analysis with other system-wide analytical methods (i.e. gene expression, protein levels, etc.).The proposed project seeks to develop an innovative cloud-based platform with an interactive modular interface that allows users to easily build customized pipelines for analysis of untargeted metabolomics data. The platform will empower the opportunity to increase the number of annotated analytes and to integrate metabolomics with other omics data, thereby enhancing the involvement of metabolomics in systems biology-based biomarker and drug discovery studies. Despite a large accumulation of metabolomics data acquired over the past several years, effective use of these data for biomarker and drug discovery has been very limited. These challenges are in part due to the lack of effective tools that: (1) accurately determine the identity of disease-associated analytes; (2) help investigate the rewiring and conserved interactions among metabolites in the progression of disease; and (3) integrate multi-omics data to evaluate the relationship between metabolites and diseases at the systems level. This project will advance scientific knowledge by investigating and evaluating innovative computational methods for metabolite annotation, differential analysis of metabolite profiles, and multi-omics data integration. Furthermore, the project will lead to a cloud-based platform that enables users to build their desired data analysis workflow or pipeline by choosing from several innovative modules to analyze metabolomics data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.