CAREER: Improved Mass Spectrometry Identification and Quantification Through Probabilistic Data Segmentation

质谱法是一种化学技术，具有广泛的社会利益应用，包括医学，法医学和基础生物科学。这项研究开发了新的分析技术，使质谱数据能够以目前不可能的方式使用，这可能会导致医学诊断、药物开发等领域的进步，并更好地了解与蛋白质有关的疾病，如阿尔茨海默病。该研究计划的一个重要部分涉及到蒙大拿州的斯雷斯旺高中，在那里，研究人员和教师将合作教授学生使用计算机解决问题的技能，科目包括化学、数学和生物。具有较强计算解决问题能力的学生更有可能进入大学，更有可能申请并成功从事需要这些技能的职业，包括科学研究。质谱（MS）在许多研究中发挥着重要作用，因为它可以量化和鉴定几乎任何细胞系统的主要成分（蛋白质，脂质，代谢物）。质谱数据处理通过分析数字质谱输出信号来识别质谱样品的分子组成。本研究开发了一种完全不同的MS输出信号分析方法：1)创建一个完全不同的MS信号处理范式，该范式将整个MS输出文件进行概率分割，而不是提取感兴趣的子区域；2)通过定量评估表明当前的方法是不够的；3)通过捕获当前被排除的低丰度分子来实现未来的研究。4)通过基于所提出的分割技术提供的附加信息的新型对应方法，展示这种新范式如何拓宽下游实验的可能性。将通过继续和扩大目前成功的实验室参与招聘实践来促进多样性。本研究通过以下方式将真实的研究经验带入大学和农村高中的课堂：1)通过将计算质谱法整合到蒙大拿大学的本科和研究生计算机科学课程中，刺激发现（特别是在代表性不足的群体中）。2)通过将计算问题解决研究整合到高中课程材料中，培养农村高中学生基于问题的STEM经验。这项研究的结果将发布在http://ms.cs.umt.edu上。

Mass spectrometry is a chemistry technique that has a broad range of applications of societal interest, including in medicine, forensics, and basic biological sciences. This research develops new analysis techniques that allow mass spectrometry data to be used in ways that are not currently possible, that may lead to advances in fields like medical diagnostics, drug development, and better understanding of poorly understood ailments involving proteins, such as Alzheimer's disease. A significant part of the research plan involves outreach to Seeley Swan High School in Montana, where researchers and teachers will team up to teach students problem-solving skills using computers, in subjects such as chemistry, math, and biology. Students who have strong computational problem-solving abilities are more likely to attend college and more likely to apply for, and succeed in, professions requiring those skills, including scientific research.Mass spectrometry (MS) plays a role in many investigations because it can quantify and identify the major components (proteins, lipids, metabolites) of almost any cellular system. MS data processing identifies the molecular composition of an MS sample by analyzing digital MS output signals. This research develops a fundamentally different approach to MS output signal analysis by: 1) Creating a fundamentally different paradigm for MS signal processing that probabilistically segments the entire MS output file instead of extracting subregions of interest, 2) Showing that current methods are insufficient through a quantitative evaluation, 3) Enabling future research by capturing currently excluded low abundance molecules, and 4) Demonstrating how this new paradigm broadens downstream experimental possibilities with a novel correspondence approach built on the additional information provided by the proposed segmentation techniques. Diversity will be fostered through continuing and expanding currently successful recruitment practices for lab participation. This research brings real research experience into collegiate and rural high school classrooms by 1) Stimulating discovery (particularly among underrepresented groups) through integrating computational mass spectrometry into the undergraduate and graduate computer science curriculum at the University of Montana. 2) Fostering problem-based STEM experience in rural high school students through integrating computational problem solving research into high school course materials. Results from this research will be posted to http://ms.cs.umt.edu.