In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow, thereby preventing valuable information from being discarded in the identification stage. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. This reduces search time due to the data reduction. We can now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Quandenser+Triqler outperforms the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins for all tested datasets. Software is available for all major operating systems at https://github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.

在shot弹枪蛋白质组学中，无标记定量实验的分析通常受限于定量数据中的识别率和噪声水平。这通常导致差异表达分析的灵敏度低。在这里，我们提出了一种针对肽的定量优先方法，该方法可以逆转经典的优先识别工作流程，从而防止有价值的信息在识别阶段被丢弃。具体来说，我们引入了一种方法Quanquanser，该方法在MS1和MS2级别上应用无监督的聚类，以汇总所有感兴趣的分析物而无需分配身份。由于数据减少，这减少了搜索时间。现在，我们可以采用开放式修改和从头搜索功能，以识别传统管道中未注意到的目标分析物。Quandenser + Triqler优于最新方法MaxQuant + Perseus，可为所有测试数据集持续报告更多差异丰富的蛋白质。根据Apache 2.0许可，可在https://github.com/statisticalbiotechnology/quandenser上针对所有主要操作系统使用该软件。