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

职业：通过概率数据分割改进质谱鉴定和定量

• 批准号: 1552240
• 负责人: Rob Smith
• 金额: $ 74.21万
• 依托单位: University of Montana
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552240&HistoricalAwards=false
• 关键词: CAREER; Improved; Mass; Spectrometry; Identification

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.

质谱学是一种化学技术，具有广泛的社会应用，包括在医学、法医学和基础生物科学中。这项研究开发了新的分析技术，允许以目前不可能的方式使用质谱学数据，这可能会导致医疗诊断、药物开发等领域的进步，并更好地了解涉及蛋白质的鲜为人知的疾病，如阿尔茨海默病。研究计划的一个重要部分涉及到蒙大拿州的Seeley Swan高中，在那里，研究人员和教师将合作教授学生在化学、数学和生物等学科中使用计算机解决问题的技能。计算问题解决能力强的学生更有可能上大学，更有可能申请需要这些技能的职业，包括科学研究。质谱学在许多研究中发挥着作用，因为它可以量化和识别几乎任何细胞系统的主要成分(蛋白质、脂类、代谢物)。MS数据处理通过分析数字MS输出信号来识别MS样品的分子组成。这项研究开发了一种根本不同的MS输出信号分析方法：1)创建一种根本不同的MS信号处理范式，对整个MS输出文件进行概率分割，而不是提取感兴趣的子区域；2)通过定量评估显示当前方法的不足；3)通过捕获当前排除的低丰度分子使未来的研究成为可能；以及4)展示这种新范式如何通过建立在建议的分割技术提供的额外信息基础上的新对应方法来扩展下游实验的可能性。将通过继续和扩大目前成功的实验室参与招聘做法来促进多样性。这项研究通过1)通过将计算质谱学整合到蒙大拿大学的本科生和研究生计算机科学课程中来激发发现(特别是在代表不足的群体中)，将真实的研究体验带到大学和农村高中的课堂上。2)将计算问题解决研究整合到高中教材中，培养农村高中生基于问题的STEM体验。这项研究的结果将发布在http://ms.cs.umt.edu.上