ETD CHARGE PREDICTION MACHINE

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。 电子转移解离(ETD)可以解离高电荷态离子。有效地分析与ETD解离的离子需要准确地确定电荷状态以计算分子量。我们创建了一种算法来分配常用于ETD的离子的电荷态。该程序名为电荷预测机(CPM)，使用贝叶斯决策理论来解释ETD中遇到的不同电荷减少过程，并且还可以处理多路光谱。CPM从四个实验的组合数据集中正确地将13097个MS2光谱中的98%指定为电荷状态。在CPM和竞争对手Charger(ThermoFisher)程序的比较中，CPM产生了一半的错误。

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. Electron transfer dissociation (ETD) can dissociate highly charged ions. Efficient analysis of ions dissociated with ETD requires accurate determination of charge states for calculation of molecular weight. We created an algorithm to assign the charge state of ions often used for ETD. The program, Charge Prediction Machine (CPM), uses Bayesian decision theory to account for different charge reduction processes encountered in ETD and can also handle multiplex spectra. CPM correctly assigned charge states to 98% of the 13 097 MS2 spectra from a combined data set of four experiments. In a comparison between CPM and a competing program, Charger (ThermoFisher), CPM produced half the mistakes.