DEFEATING MS1 UNDERSAMPLING WITH ACCURATE MASS AND TIME TAGS

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 与南伊利诺伊州生物信息学家程强教授合作 卡本代尔大学，已经发起了一项旨在开发一种机器的计划 用于检测革兰氏酶裂解产物的学习方法。这一方法将 使用来自2D凝胶斑点的串联MS数据来识别候选蛋白质列表，然后 准确的质量和时间MS1数据将被映射到之前识别的序列上 机器学习算法试图识别切割产物。这是意料之中的 这种方法将解决与数据相关的欠采样问题 依赖于串联式MS数据的获取，从而提高了 已完成的方法。

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.