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: 1933305
• 负责人: Travis Wheeler
• 金额: $ 72.54万
• 依托单位: University of Montana
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933305&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上的进步。质谱仪是用于测量此类样品的关键设备之一，而软件则用于将实验测量结果转化为可理解的信息。该项目将开发两种新的软件方法来鉴定和量化生物样品中更广泛的分子含量。每种解决方案都旨在增加可提取的分子测量数量和对测量准确性的信心，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。这有可能使基础生物科学（如细胞生物学）以及医学等应用领域取得突破，在这些领域，每次测定的信息量的限制提高了发现成本。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进入STEM培训的途径。通过多个级别的课程模块，包括一些旨在帮助高中生过渡到计算机科学和计算生物学本科课程的课程模块，将促进对STEM原则的正式理解。特别是，高中生将学习第一年计算机科学课程中所要求的基本编程知识。质谱仪是一种硬件探测器，用于观察与样品中分子的身份和数量相对应的信号，包括液体、固体或气相中的分子。仪器输出需要信号处理步骤，以使结果可解释。生物样品的复杂性质导致质谱信号的相当大的重叠，因此在识别单个分子物种方面存在相当大的计算挑战。由于样品中分子数量和组成的组合性质，目前的质谱解释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值。当样品包含多种类型的分子、存在具有相似化学特性的分子以及分子丰度较低时，这限制了方法的有效性。新的算法有潜力克服这些数据处理的限制。该项目将开发两种新的计算方法，用于识别可以在可处理时间内从样品产生给定光谱的完整可能的生物分子集。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，无论是修饰的还是未修饰的蛋白质，都可以测试当前方法局限性所排除的许多生物学假设。该研究的成功完成将通过创建公开可用的高级数据处理算法，为科学家提供对质谱实验中目前未使用的信息的扩展访问。此外，该项目还包括两个明确的外展计划，通过计算机科学和计算生物学新学生的课程以及本科生和研究生的研究经验机会，使生物信息学更广泛地参与其中。该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响评估标准进行了评估，认为值得支持。