Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra.

中文翻译：

---

用于质谱蛋白质组学中无诱饵错误发现率估计的新混合模型

准确估计光谱识别的错误发现率 (FDR) 是基于质谱的蛋白质组学的核心问题。在过去的二十年中，目标诱饵方法（TDAs）和无诱饵方法（DFA）已被广泛用于估计 FDR。TDA 使用诱饵物种数据库来忠实地模拟不正确的肽谱匹配 (PSM) 的分数分布。另一方面，DFA 拟合双组分混合模型来学习正确和不正确的 PSM 分数分布的参数。虽然概念上很简单，但这两种方法在实践中都会出现问题，特别是在将仪器推向极限并产生低碎裂效率和低信噪比光谱的实验中。