SBIR Phase I: Technical R&D for cloud-based high-information mass spectrometry data extraction commercialization

这个小型企业创新研究（SBIR）项目的更广泛的影响/商业潜力是建立更快、更便宜、更准确的方法来确定生物样品的含量，从而产生新的医学诊断、新药和其他目前采用质谱法的科学方法无法企及的新产品。该项目将开发技术，为质谱分析提供更好的软件，并将使更广泛的学术和工业科学家更容易获得基于质谱的样品分析，从而打破1亿美元的市场。需要先进的软件来将原始实验数据转换为分子和数量清单，以创造新药、发现疾病和增加粮食产量。该提案目标的成功完成将使下一代质谱软件的开发成为可能，提供基于先进、高信息算法的用户友好、快速的云软件。该SBIR一期项目建议开发用于质谱数据处理的新型高信息软件技术。第一阶段的拟议工作将为第二阶段的原型开发和测试提供概念验证。该研究将解决三个关键的技术障碍，这是原型开发的先决条件：1)质谱数据存储和检索架构足以处理典型研究中数百个文件中的更多信息，2)云优化版本的质谱信息提取算法，以及3)基于云的模型-视图控制器框架，克服网络特定的质谱数据处理挑战。目标是在不增加成本的情况下，在相同的时间内进行更多的实验，从相同的实验中识别出比现有方法更多的分子，并检测到较少的分子，而现有方法无法检测到。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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/commercial potential of this Small Business Innovation Research (SBIR) project is to establish faster, less expensive, more accurate ways of determining the contents of biological samples leading to novel medical diagnostics, new drugs, and other new products beyond the reach of the current scientific methods that employ mass spectrometry. The project will develop technology that enables better software for mass spectrometry, and will make mass spectrometry-based sample analysis more accessible to a broader segment of academic and industrial scientists, disrupting a $100M market. Advanced software is required to transform raw experimental data into the lists of molecules and quantities needed to create new drugs, discover diseases and increase food production. The successful completion of the aims of this proposal will enable the development of software built for the next generation of mass spectrometry, providing user-friendly, fast cloud software built on advanced, high-information algorithms.This SBIR Phase I project proposes to develop novel, high-information software technology for mass spectrometry data processing. The proposed work in Phase I will provide proof-of-concept for prototype development and testing in Phase II. The research will address three critical technical hurdles that are prerequisites to prototype development: 1) A mass spectrometry data storage and retrieval architecture sufficient to handle significantly more information from the hundreds of files in a typical study, 2) cloud-optimized versions of mass spectrometry information extraction algorithms, and 3) a cloud-based model-view controller framework that overcomes web-specific challenges for mass spectrometry data processing. The goal is to allow more experiments in the same amount of time without increasing costs, identifying more molecules than existing methods from the same experiment, and detecting molecules that are less abundant and go undetected by current methods.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.