State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, PR China.
Proteomics. 2010 Dec;10(23):4293-300. doi: 10.1002/pmic.200900668.
The probability-based search engine MASCOT has been widely used to identify peptides and proteins in shotgun proteomic research. Most subsequent quality control methods filter out ambiguous assignments according to the ion score and thresholds provided by MASCOT. On the basis of target-decoy database search strategy, we evaluated the performance of several filter methods on MASCOT search results and demonstrated that using filter boundaries on two-dimensional feature spaces, the MASCOT ion score and its relative score can improve the sensitivity of the filter process. Furthermore, using a linear combination of several characteristics of the assigned peptides, including the MASCOT scores, 15 previously employed features, and some newly introduced features, we applied a Bayesian nonparametric model to MASCOT search results and validated more correctly identified peptides in control and complex data sets than those could be validated by empirical score thresholds.
基于概率的搜索引擎 MASCOT 已被广泛用于鉴定 shotgun 蛋白质组学研究中的肽和蛋白质。大多数后续的质量控制方法根据 MASCOT 提供的离子得分和阈值过滤掉不确定的分配。在目标诱饵数据库搜索策略的基础上，我们评估了几种过滤方法在 MASCOT 搜索结果上的性能，并证明了在二维特征空间上使用过滤边界，MASCOT 离子得分及其相对得分可以提高过滤过程的灵敏度。此外，使用分配肽的几个特征的线性组合，包括 MASCOT 得分、15 个以前使用的特征和一些新引入的特征，我们将贝叶斯非参数模型应用于 MASCOT 搜索结果，并在控制和复杂数据集上验证了比经验得分阈值可以验证的更多正确鉴定的肽。
