无标记蛋白质组定量(LFQ)是由定量工具和随后的数据处理方法共同定义的多步骤工作流程,已广泛应用于当前的生物医学领域。农业,和环境研究。尽管最近取得了进展,深入和高质量的量化仍然极具挑战性,需要通过比较评估LFQ的性能来优化LFQ。然而,使用不同标准(精度,准确度,和鲁棒性)差异很大,大量潜在的LFQ成为全面优化蛋白质组定量的瓶颈之一。在这项研究中,一种新颖的策略,能够从数千个工作流程中发现同时增强性能的LFQ(集成18个量化工具和3,128个操作链),因此被提议。首先,实现精度同步提高的可行性,准确度,通过共同优化LFQ的多步操作链,系统地评估了LFQ的稳健性。第二,基于通过不同采集模式的各种量化测量获得的各种基准数据集,这种新颖的策略成功地确定了许多操作链,这些操作链同时提高了跨多个标准的性能。最后,为了进一步增强蛋白质组定量并发现最佳性能的LFQ,开发了一个在线工具(https://idrblab.org/anpela/),可以对整个LFQ工作流程进行集体绩效评估(从多个角度)。这项研究证实了实现精度同步提高的可行性,准确度,和鲁棒性。本研究中提出和验证的新策略以及在线工具可能会为需要基于质谱的LFQ技术的研究领域提供有用的指导。
The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.