DEFEATING MS1 UNDERSAMPLING WITH ACCURATE MASS AND TIME TAGS

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 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，已经开始与开发一种机器的意图， 用于检测gramzyme切割产物的学习方法。该办法将 使用来自2D凝胶点的串联MS数据来鉴定候选蛋白质的列表，然后 精确的质量和时间MS 1数据将被映射到识别的序列上， 机器学习算法试图识别裂解产物。预计 这种方法将解决与数据相关的抽样不足问题， 串联MS数据的依赖性采集，并因此增加 已经完成的方法。

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.