DEFEATING MS1 UNDERSAMPLING WITH ACCURATE MASS AND TIME TAGS

使用精确的质量和时间标签击败 MS1 欠采样

• 批准号: 8361421
• 负责人: Qiang Cheng
• 金额: $ 0.4万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8361421
• 关键词: Algorithms; Collaborations; Data; Detection; Funding; Gel; Grant; Illinois; Intention; Machine Learning; Maps; Mass Spectrum Analysis; Methods; National Center for Research Resources; Principal Investigator; Proteins; Research; Research Infrastructure; Resources; Sampling; Source; Spottings; Time; United States National Institutes of Health; Universities; biomedical resource; combat; cost; professor

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. A collaboration with professor Qiang Cheng, a bioinformatician at Southern Illinois University in Carbondale, has been initiated with the intention of developing a machine learning approach for the detection of gramzyme cleavage products. The approach will use tandem MS data from 2D gel spots to identify a list of candidate proteins and then accurate mass and time MS1 data will be mapped onto the identified sequences before a machine learning algorithm attempts to identify the cleavage products. It is expected that this approach will combat the under sampling issue associated with data dependent acquisition of tandem MS data, and thus increase the sensitivity of an already completed method.

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的主要研究者可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 与南伊利诺伊州生物信息学家程强教授的合作 卡本代尔大学的成立目的是开发一种机器 检测革酶裂解产物的学习方法。该方法将 使用来自 2D 凝胶点的串联 MS 数据来识别候选蛋白质列表，然后 准确的质量和时间 MS1 数据将在之前映射到识别的序列上 机器学习算法尝试识别裂解产物。预计 这种方法将解决与数据相关的采样不足问题 串联 MS 数据的依赖性采集，从而提高了分析的灵敏度 已经完成的方法。