Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of ‘Protein Presence,’ an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from https://bitbucket.org/lababi/protyquant/.

比较多个无标记shot弹枪蛋白质组学数据集需要各种数据处理和格式化步骤，包括肽谱匹配，蛋白质推断和定量。最后，将结果文件编译为允许下游分析的格式。ProtyQuant进行蛋白质推断和定量计算，并将单个数据集的结果合并到纯文本表中。这些是轻量级的，易于阅读的并且易于导入数据库或统计软件。ProtyQuant从蛋白质组工作流（例如跨蛋白质组学管道（TPP））读取经过验证的pepXML，这使其与许多商业和免费搜索引擎兼容。为了进行蛋白质推断和定量，整合了PIPQ程序的修改版本（He et al。2016）。与简单的频谱计数相比，PIPQ总结了肽的概率。为了将肽分配给蛋白质，可以使用三种算法：多重计数，等分和线性规划。累积的肽概率（app）用于任务，蛋白质概率估计和定量。使用参考数据集测试了ProtyQuant的无标签shot弹枪蛋白质组学，该蛋白质组学是从掺入酵母裂解物中的48种不同浓度的人UPS蛋白获得的。与ProteinProphet相比，ProtyQuant在混合物中检测出多达126种（15％）更多的蛋白质，并具有相同的假阳性率（FPR）。使用应用程序值进行无标签定量显示了合适的灵敏度和线性。引人注目的是，应用程序值代表了“蛋白质存在”的真实度量，这是蛋白质概率和数量的一个完整概念。ProtyQuant提供了图形用户界面（GUI）和脚本，用于基于控制台的处理。可从https://bitbucket.org/lababi/protyquant/在Windows，Linux和Docker上使用（GNU GLP v3）。