Machine learning analysis of tandem mass spectra

描述(由申请人提供)：项目摘要：蛋白质组学领域的核心技术质谱学有望使科学家能够识别和量化组成复杂生物样本的全部分子。在生物和健康科学中，通常以近通量的方式使用质谱仪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。现有的用于解释质谱图的算法速度很慢，并且不能识别很大比例的给定谱。我们建议将机器学习领域的技术和工具应用于质谱学数据的分析。我们将在质谱仪中建立多肽裂解的计算模型，以及整个质谱学过程的更大规模的模型。使用这些模型，我们将设计和验证算法，以识别最能解释观察到的一组光谱的蛋白质组。所有这些方法的软件实施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、更高效、更准确地分析和理解他们的质谱学数据。相关性：质谱学的应用及其对改善人类健康的前景是多种多样的，包括对疾病表型及其背后的分子机制的了解增加，以及更敏感和更具体的诊断和预后筛查。

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.