arXiv 2009.080232020-09-17q-bio.QM

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

New mixture models for decoy-free false discovery rate estimation in mass-spectrometry proteomics

Yisu Peng, Shantanu Jain, Yong Fuga Li, Michal Gregus, Alexander R. Ivanov, Olga Vitek, Predrag Radivojac

摘要：

动机：准确估计光谱鉴定的错误发现率（FDR）是基于质谱的蛋白质组学中的一个核心问题。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。TDAs使用诱饵物种的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导致问题，特别是在将仪器推向极限并产生低碎片效率和低信噪比光谱的实验中。结果如下：我们引入了一个新的无诱饵的FDR估计框架，概括了目前的DFA，同时利用更多的搜索数据，以类似于TDAs的方式。我们的方法依赖于多组分混合物，其中得分分布对应于正确的PSM，最好的不正确的PSM，和第二个最好的不正确的PSM建模的偏正态家庭。我们推导出EM算法来估计这些分布的参数，从与每个实验光谱相关的最佳和第二佳PSM的分数。我们在多个蛋白质组学数据集和一个HeLa细胞消化案例研究中评估了我们的模型，该案例研究总共包含100多万个光谱。我们提供的证据表明，与现有的DFA相比，性能得到了改善，与TDAs相比，稳定性和速度得到了改善，而性能没有任何下降。我们建议，新的策略有可能扩展到肽鉴定之外，并减少所有分析平台上对TDA的需求。

英文摘要：

Motivation: 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. Results: We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs, and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms.

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英文摘要：

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