DEFEATING MS1 UNDERSAMPLING WITH ACCURATE MASS AND TIME TAGS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.