Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China.
Westlake Omics, Ltd, Hangzhou, Zhejiang Province, China.
Mol Cell Proteomics. 2023 Sep;22(9):100623. doi: 10.1016/j.mcpro.2023.100623. Epub 2023 Jul 21.
Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.
基于数据非依赖性采集(DIA)的质谱蛋白质组学可生成可重现的蛋白质组数据。DIA 数据的复杂处理导致了多种数据分析工具的发展。在这项研究中,我们使用来自 TripleTOF、Orbitrap 和 TimsTOF Pro 仪器的六个 DIA 数据集评估了五种工具(OpenSWATH、EncyclopeDIA、Skyline、DIA-NN 和 Spectronaut)的性能。通过比较鉴定和定量指标,并检查跨工具的共享和独特鉴定,我们评估了基于库和无库的方法。我们的研究结果表明,当光谱库的综合性有限时,无库方法优于基于库的方法。然而,我们的结果还表明,构建一个全面的库仍然为大多数 DIA 分析提供了好处。这项研究为 DIA 数据分析工具提供了全面的指导,使 DIA-质谱技术的有经验和新手用户都受益。