Zhang Jiyang, Li Jianqi, Xie Hongwei, Zhu Yunping, He Fuchu
College of Mechanical and Electronic Engineering and Automatization, National University of Defense Technology, Changsha, China.
Proteomics. 2007 Nov;7(22):4036-44. doi: 10.1002/pmic.200600929.
Based on the randomized database method and a linear discriminant function (LDF) model, a new strategy to filter out false positive matches in SEQUEST database search results is proposed. Given an experiment MS/MS dataset and a protein sequence database, a randomized database is constructed and merged with the original database. Then, all MS/MS spectra are searched against the combined database. For each expected false positive rate (FPR), LDFs are constructed for different charge states and used to filter out the false positive matches from the normal database. In order to investigate the error of FPR estimation, the new strategy was applied to a reference dataset. As a result, the estimated FPR was very close to the actual FPR. While applied to a human K562 cell line dataset, which is a complicated dataset from real sample, more matches could be confirmed than the traditional cutoff-based methods at the same estimated FPR. Also, though most of the results confirmed by the LDF model were consistent with those of PeptideProphet, the LDF model could still provide complementary information. These results indicate that the new method can reliably control the FPR of peptide identifications and is more sensitive than traditional cutoff-based methods.
基于随机数据库方法和线性判别函数（LDF）模型，提出了一种在SEQUEST数据库搜索结果中滤除假阳性匹配的新策略。给定一个实验性的MS/MS数据集和一个蛋白质序列数据库，构建一个随机数据库并与原始数据库合并。然后，针对合并后的数据库搜索所有MS/MS谱图。对于每个预期的假阳性率（FPR），针对不同电荷态构建LDF，并用于从正常数据库中滤除假阳性匹配。为了研究FPR估计的误差，将新策略应用于一个参考数据集。结果，估计的FPR与实际FPR非常接近。当应用于人类K562细胞系数据集（这是一个来自真实样本的复杂数据集）时，在相同的估计FPR下，与传统的基于截断值的方法相比，可以确认更多的匹配。此外，尽管LDF模型确认的大多数结果与PeptideProphet的结果一致，但LDF模型仍可提供补充信息。这些结果表明，新方法可以可靠地控制肽段鉴定的FPR，并且比传统的基于截断值的方法更灵敏。
