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

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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.