Overcoming combinatoric complexity problems in computational mass spectrometry

克服计算质谱中的组合复杂性问题

• 批准号: 2312016
• 负责人: Travis Wheeler
• 金额: $ 72.54万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312016&HistoricalAwards=false
• 关键词: Overcoming; combinatoric; complexity; problems; computational

Many biological advances could be made were it possible to better identify and quantify the molecular contents of biological samples. Mass spectrometers are one key device used for measuring such samples, and software is used to translate experimental measurements into understandable information. This project will develop two new software methods to identify and quantify a wider range of the molecular contents of biological samples. Each solution aims to increase both the number of molecular measurements that can be extracted and the confidence in the accuracy of the measurements, allowing scientists who rely on mass spectrometry to answer more experimental questions than is possible with current software tools. This has the potential to enable breakthroughs in basic biological sciences such a cell biology, as well as applied fields like medicine, where limitations in the amount of information per assay drives up the cost of discovery. In addition to facilitating scientific progress, this project will provide pathways into STEM training for students through industry internships at the graduate and undergraduate levels. Formal understanding of the STEM principles will be facilitated through coursework modules at a number of levels, including some that aim to help high school students transition to enrollment in undergraduate computer science and computational biology programs. In particular, high-school students will learn aspects of basic programming literacy expected in first-year computer science courses.Mass spectrometers are hardware detectors that observe signal corresponding to the identities and quantities of molecules in a sample, including those in the liquid, solid, or gaseous phase. Instrumental output requires signal processing steps to render results interpretable. The complex nature of biological samples leads to considerable overlap in the mass spec signals and hence considerable computational challenges in identifying individual molecular species. Because of the combinatorial nature of the number and composition of the molecules in samples, current algorithms for mass spectrometry interpretation do not scale well, so they generally sub-select and analyze only a few of the measurements. This limits the effectiveness of the method when samples include multiple types of molecules, when molecules having similar chemical characteristics are present, and when molecules are low in abundance. New algorithms have the potential to overcome these data processing limitations. This project will develop two novel computational approaches for identifying the complete set of possible biomolecules that could produce a given spectrum from a sample in tractable time. The first approach will focus on leveraging data patterns to extract latent information from mass spectrometry tandem mass spectra. The second approach will use novel data representations to shrink the search space for potential pattern matches in protein sequences. These algorithms will increase coverage, accuracy, and sensitivity of proteomics results from mass spectrometry measurements, for both modified and unmodified proteins, enabling testing of numerous biological hypotheses precluded by the limitations of current methods. The successful completion of the research will provide expanded access to the currently unused information in mass spectrometry experiments for scientists by creating publicly available advanced algorithms for data processing. Additionally, the project includes two explicit outreach programs to enable broader participation in bioinformatics through curriculum for students new to computer science and computational biology and research experience opportunities at the undergraduate and graduate levels. The results of this project will be posted at ms.cs.umt.edu.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.

如果能够更好地鉴定和量化生物样品的分子含量，就可以取得许多生物学进展。质谱仪是用于测量此类样品的一种关键设备，软件用于将实验测量结果转换为可理解的信息。该项目将开发两种新的软件方法，以识别和量化生物样品中更广泛的分子含量。每个解决方案都旨在增加可以提取的分子测量数量和测量准确性的信心，使依赖质谱的科学家能够回答比当前软件工具更多的实验问题。这有可能在细胞生物学等基础生物科学以及医学等应用领域实现突破，在这些领域，每次检测的信息量限制会推高发现成本。除了促进科学进步外，该项目还将通过研究生和本科生的行业实习为学生提供STEM培训的途径。对STEM原则的正式理解将通过多个级别的课程模块来促进，包括一些旨在帮助高中学生过渡到本科计算机科学和计算生物学课程的课程模块。特别是，高中生将学习第一年计算机科学课程所需的基本编程知识。质谱仪是一种硬件检测器，用于观察与样品中分子的身份和数量相对应的信号，包括液相，固相或气相中的分子。仪器输出需要信号处理步骤来使结果可解释。生物样品的复杂性质导致质谱信号的相当大的重叠，因此在鉴定单个分子种类时存在相当大的计算挑战。由于样品中分子的数量和组成的组合性质，目前用于质谱解释的算法不能很好地扩展，因此它们通常只选择和分析少数测量结果。当样品包括多种类型的分子时，当存在具有相似化学特性的分子时，以及当分子丰度低时，这限制了该方法的有效性。新算法有可能克服这些数据处理限制。该项目将开发两种新的计算方法，用于识别一整套可能的生物分子，这些生物分子可以在易于处理的时间内从样品中产生给定的光谱。第一种方法将侧重于利用数据模式从质谱串联质谱中提取潜在信息。第二种方法将使用新的数据表示来缩小蛋白质序列中潜在模式匹配的搜索空间。这些算法将增加质谱测量的蛋白质组学结果的覆盖率，准确性和灵敏度，对于修饰的和未修饰的蛋白质，使得能够测试许多生物学假设，这些假设被当前方法的限制所排除。这项研究的成功完成将通过创建公开的数据处理先进算法，为科学家提供更多的质谱实验中目前未使用的信息。此外，该项目包括两个明确的推广计划，使更广泛地参与生物信息学，通过课程为学生新的计算机科学和计算生物学和研究经验的机会，在本科和研究生水平。该项目的结果将在ms.cs.umt.edu上公布。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。