Peptide identification quality control
Peptide identification quality control
复制标题
DOI：
10.1002/pmic.201000704
复制
发表时间：
2011-05-01
期刊：
影响因子：
3.4
通讯作者：
Zahedi, Rene P.
中科院分区：
文献类型：
--
作者：
Vaudel, Marc;Burkhart, Julia M.;Zahedi, Rene P.
大型蛋白质组学数据集的识别通常使用称为搜索引擎的复杂软件工具进行。然而，尽管识别过程很重要，但其配置和执行通常是根据既定的实验室习惯进行的，并且大多没有详细的质量控制监督。为了建立可广泛应用于鉴别过程的易于获得的质量控制标准，我们在这里介绍几种简单的质量控制方法。将使用目标/诱饵搜索对识别参数进行无偏质量控制，从而大大改进识别标准。例如，在恒定的置信度水平下，吉祥物的识别增加了13%。然而，目标/诱饵方法不能普遍适用。因此，我们还对该策略本身的应用进行了质量控制，为评估获得的错误发现率的精度和鲁棒性提供了有用和直观的指标。
Identification of large proteomics data sets is routinely performed using sophisticated software tools called search engines. Yet despite the importance of the identification process, its configuration and execution is often performed according to established lab habits, and is mostly unsupervised by detailed quality control. In order to establish easily obtainable quality control criteria that can be broadly applied to the identification process, we here introduce several simple quality control methods. An unbiased quality control of identification parameters will be conducted using target/decoy searches providing significant improvement over identification standards. MASCOT identifications were for instance increased by 13% at a constant level of confidence. The target/decoy approach can however not be universally applied. We therefore also quality control the application of this strategy itself, providing useful and intuitive metrics for evaluating the precision and robustness of the obtained false discovery rate.