Machine learning analysis of tandem mass spectra

项目概述：质谱是蛋白质组学领域的核心技术，有望使科学家能够识别和量化组成复杂生物样品的整个分子补体。在生物和健康科学中，质谱法通常用于近通量方式来鉴定混合物中的蛋白质。目前，这类实验的主要瓶颈是计算。现有的解释质谱的算法速度很慢，并且不能识别给定光谱的很大一部分。我们建议将机器学习领域的技术和工具应用于质谱数据的分析。我们将在质谱仪内建立肽片段的计算模型，以及整个质谱过程的更大规模模型。使用这些模型，我们将设计和验证算法，以识别一组蛋白质，最好地解释一组观察到的光谱。所有方法的软件实现将以用户友好的形式公开提供。实际上，该软件将使科学家能够更容易，有效和准确地分析和理解他们的质谱数据。相关性：质谱的应用及其对改善人类健康的承诺是多方面的，包括对疾病表型及其背后的分子机制的进一步了解，以及更加敏感和特异性的诊断和预后筛查。

DESCRIPTION (provided by applicant): Project summary: Mass spectrometry, the core technology in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of molecules that comprise a complex biological sample. In the biological and health sciences, mass spectrometry is commonly used in a nigh-throughput fashion to identify proteins in a mixture. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We propose to apply techniques and tools from the field of machine learning to the analysis of mass spectrometry data. We will build computational models of peptide fragmentation within the mass spectrometer, as well as larger-scale models of the entire mass spectrometry process. Using these models, we will design and validate algorithms for identifying the set of proteins that best explain an observed set of spectra. Software implementations for all of the methods will be made publicly available in a user-friendly form. In practical terms, this software will enable scientists to more easily, efficiently and accurately analyze and understand their mass spectrometry data. Relevance: The applications of mass spectrometry and its promises for improvements of human health are numerous, including an increased understanding of disease phenotypes and the molecular mechanisms that underlie them, and vastly more sensitive and specific diagnostic and prognostic screens.