Statistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modeling
Statistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modeling
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DOI：
10.1021/pr7006818
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发表时间：
2008-01-01
影响因子：
4.4
通讯作者：
Nesvizhskii, Alexey I.
中科院分区：
文献类型：
--
作者：
Choi, Hyungwon;Ghosh, Debashis;Nesvizhskii, Alexey I.
肽和蛋白质鉴定的可靠统计验证是基于大规模质谱的蛋白质组学的首要任务。PeptideProphet是常用的计算工具之一，用于评估使用数据库搜索程序（如SEQUEST、MASCOT或X！串联。我们提出了两种灵活的方法，可变组分混合模型和半参数混合模型，去除限制性参数假设的混合建模方法的肽先知。使用线性离子阱傅立叶变换（LTQ-FT）质谱仪上生成的控制蛋白质混合物数据集，我们证明了这两种方法在概率估计的准确性和检测正确识别的能力方面提高了参数模型，并将错误发现率控制在相同程度上。这里提出的统计方法要求数据集包含足够数量的诱饵（已知是不正确的）肽鉴定，这可以使用目标诱饵数据库搜索策略获得。
Reliable statistical validation of peptide and protein identifications is a top priority in large-scale mass spectrometry based proteomics. PeptideProphet is one of the computational tools commonly used for assessing the statistical confidence in peptide assignments to tandem mass spectra obtained using database search programs such as SEQUEST, MASCOT, or X! TANDEM. We present two flexible methods, the variable component mixture model and the semiparametric mixture model, that remove the restrictive parametric assumptions in the mixture modeling approach of PeptideProphet. Using a control protein mixture data set generated on an linear ion trap Fourier transform (LTQ-FT) mass spectrometer, we demonstrate that both methods improve parametric models in terms of the accuracy of probability estimates and the power to detect correct identifications controlling the false discovery rate to the same degree. The statistical approaches presented here require that the data set contain a sufficient number of decoy (known to be incorrect) peptide identifications, which can be obtained using the target-decoy database search strategy.