Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.
Science for Life Laboratory, KTH Royal Institute of Technology, Solna, Sweden.
Methods Mol Biol. 2023;2426:91-117. doi: 10.1007/978-1-0716-1967-4_5.
Protein quantification for shotgun proteomics is a complicated process where errors can be introduced in each of the steps. Triqler is a Python package that estimates and integrates errors of the different parts of the label-free protein quantification pipeline into a single Bayesian model. Specifically, it weighs the quantitative values by the confidence we have in the correctness of the corresponding PSM. Furthermore, it treats missing values in a way that reflects their uncertainty relative to observed values. Finally, it combines these error estimates in a single differential abundance FDR that not only reflects the errors and uncertainties in quantification but also in identification. In this tutorial, we show how to (1) generate input data for Triqler from quantification packages such as MaxQuant and Quandenser, (2) run Triqler and what the different options are, (3) interpret the results, (4) investigate the posterior distributions of a protein of interest in detail, and (5) verify that the hyperparameter estimations are sensible.
用于鸟枪法蛋白质组学的蛋白质定量是一个复杂的过程，在每个步骤中都可能引入误差。Triqler 是一个 Python 包，它可以将无标记蛋白质定量管道的不同部分的误差估计和整合到一个单一的贝叶斯模型中。具体来说，它根据我们对相应 PSM 正确性的信心来加权定量值。此外，它以一种反映缺失值相对于观测值不确定性的方式处理缺失值。最后，它将这些误差估计值组合到一个单一的差异丰度 FDR 中，该 FDR 不仅反映了定量和鉴定中的误差和不确定性。在本教程中，我们将展示如何（1）从 MaxQuant 和 Quandenser 等定量软件包中为 Triqler 生成输入数据，（2）运行 Triqler 以及有哪些不同的选项，（3）解释结果，（4）详细研究感兴趣的蛋白质的后验分布，以及（5）验证超参数估计是否合理。
