Optimization and joint modeling for peptide detection by tandem mass spectrometry

串联质谱肽检测的优化和联合建模

• 批准号: 9214942
• 负责人: William Stafford Noble
• 金额: $ 33.23万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9214942
• 关键词: Algorithms; Amino Acid Sequence; Automobile Driving; Biological; Cells; Collection; Column Chromatography; Communities; Complex; Complex Mixtures; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Detection; Diagnostic; Discipline; Economics; Fertilization; Game Theory; Health; Human; Hybrids; Joints; Libraries; Liquid Chromatography; Machine Learning; Mass Spectrum Analysis; Methodology; Methods; Modeling; Molecular; Natural Language Processing; Operations Research; Peptides; Population; Post-Translational Protein Processing; Proteins; Proteomics; Protocols documentation; Sampling; Scheme; Shotguns; Speed; Statistical Models; Time; Variant; Work; computer based statistical methods; computerized tools; cost; disease phenotype; experimental study; improved; innovation; learning strategy; liquid chromatography mass spectrometry; mass spectrometer; mathematical theory; novel; prognostic; protein aminoacid sequence; speech recognition; statistics; tandem mass spectrometry; theories; tool

Project Summary/Abstract Proteins are the primary functional molecules in living cells, and tandem mass spectrometry provides the most efficient means of studying proteins in a high-throughput fashion. The proposal aims to use state-of-the-art methods from the fields of machine learning, statistics, and natural language processing to improve our ability to make sense of large tandem mass spectrometry data sets. Our project will focus on three key problems in the analysis of such data: 1. facilitating the use of previously annotated spectra to improve our ability to annotate new spectra by creating a hybrid search scheme that compares an observed spectrum to a database comprised of theoretical spectra and previously annotated spectra, 2. enabling the efficient and accurate detection of peptides containing post-translational modifications and sequence variants, and 3. detecting sets of peptide species that are co-fragmented in the mass spectrometer and hence give rise to complex, mixture spectra. Each of these aims will improve the ability of mass spectrometrists to efficiently and accurately identify and quantify proteins in complex mixtures. To increase the impact of our work, we will continue to make all of our tools available as free software.

项目摘要/摘要 蛋白质是活细胞中的主要功能分子，串联质谱法提供了最多的 以高通量方式研究蛋白质的有效手段。该提案旨在使用最先进的 来自机器学习，统计和自然语言处理领域的方法，以提高我们的能力 理解大型串联质谱数据集。我们的项目将重点介绍三个关键问题 此类数据的分析： 1。促进使用先前注释的光谱来提高我们通过创建新光谱注释的能力 一种混合搜索方案，将观察到的频谱与理论频谱的数据库进行比较 和以前注释的光谱， 2。能够对含有翻译后修改的肽的有效检测和准确检测 序列变体和 3。检测在质谱仪中共碎片的胡椒物种的集合，因此产生 复杂的混合光谱。 这些目标中的每一个都将提高质谱学家有效，准确地识别和量化的能力 复杂混合物中的蛋白质。为了增加我们的工作影响，我们将继续使所有工具可用 作为免费软件。