Object Oriented Data Analysis for Untargeted Metabolomics

项目总结/摘要 在这个第二阶段SBIR应用程序面向对象的数据分析非靶向代谢组学，我们 建议继续第一阶段的研究，开发软件平台。在第一阶段， 功能面向对象的数据分析（fOODA），它对原始RT x m/z x离子强度数据建模， 函数形式（12-14），可以应用于原始代谢组学数据，以自动地，并且以无偏的方式， 找出受试者亚组之间的峰值差异，并以清晰的方式向研究者展示结果。的 软件在AWS云上并行运行速度很快（几个小时就可以处理数百个示例文件）。 需求工程通过用户讨论确定了将在第二阶段添加的功能（例如， 自动识别同位素、加合物、产物离子和离子抑制伪影;将结果链接到 注释数据库;增加剂量反应研究的分析）。目前的软件提供了一个初始 这一架构将得到进一步发展。 提出了三个目标：进一步开发统计和注释工具，并在模拟中验证它们， 3项非靶向代谢组学研究（目标1）;对100名 代谢组学核心主任、研究人员和临床医生（目标2）;编写软件销售和安装在核心 设施（目标3）。第一个目标建立在第一阶段项目的成功基础上，旨在优化 分析工具。另外两个目标是专注于开发商业产品。每个目标都有一个新员工 确保我们拥有实现这些目标所需的技能和专业知识。 商业化计划描述了短期和长期的业务发展。在短期内，我们将 将专利软件授权给代谢组学核心，并提供统计咨询服务。从长远来看，我们 将利用我们与多组学核心的联系，参与BioRankings多组学平台， 代谢组学、微生物组学和临床数据可以很容易地结合起来进行翻译统计分析。这 将为生物排名提供第二个收入来源。

PROJECT SUMMARY/ABSTRACT In this Phase II SBIR application for Object Oriented Data Analysis for Untargeted Metabolomics, we propose to continue the research initiated in Phase I developing a software platform. In Phase I we showed functional Object Oriented Data Analysis (fOODA), which models raw RT x m/z x Ion Intensity data in functional form (12-14), can be applied to raw metabolomics data to automatically, and in an unbiased way, find peak differences across subject subgroups, and present the results to investigators in a clear way. The software runs fast (a few hours to process hundreds of sample files) in parallel on the AWS cloud. Requirements engineering through user discussions identified functions that will be added in Phase II (e.g., automate identification of isotopes, adducts, product ions, and ion suppression artifacts; link results to annotated databases; add analyses for dose response studies). The current software provides an initial architecture that will be further developed. Three aims are proposed: further develop statistical and annotation tools and validate them in simulations and 3 untargeted metabolomics studies (Aim 1); conduct extensive Requirements Engineering interviews with 100 metabolomics core directors, researchers, and clinicians (Aim 2); and write software to sell and install in core facilities (Aim 3). The first aim builds on the success of the Phase I project and is designed to optimize the analytic tools. The other two aims are focused on developing the commercial product. A new hire for each aim will be done to ensure we have the right skill set and expertise for these aims. The commercialization plan describes short- and long-term business development. In the short-term we will license proprietary software to metabolomics cores and offer statistical consulting services. In the long-term we will leverage our contacts with multi-omics cores to participate in BioRankings multi-omics platform where metabolomics, microbiome, and clinical data can be easily combined for translational statistical analysis. This will provide a second revenue stream for BioRankings.