Data-independent acquisition (DIA) has become a well-established method for MS-based proteomics. However, the list of options to analyze this type of data is quite extensive, and the use of spectral libraries has become an important factor in DIA data analysis. More specifically the use of in silico predicted libraries is gaining more interest. By working with a differential spike-in of human standard proteins (UPS2) in a constant yeast tryptic digest background, we evaluated the sensitivity, precision, and accuracy of the use of in silico predicted libraries in data DIA data analysis workflows compared to more established workflows. Three commonly used DIA software tools, DIA-NN, EncyclopeDIA, and Spectronaut, were each tested in spectral library mode and spectral library-free mode. In spectral library mode, we used independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to classical data-dependent acquisition (DDA)-based spectral libraries. In total, we benchmarked 12 computational workflows for DIA. Our comparison showed that DIA-NN reached the highest sensitivity while maintaining a good compromise on the reproducibility and accuracy levels in either library-free mode or using in silico predicted libraries pointing to a general benefit in using in silico predicted libraries.

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计算机预测光谱库在数据独立采集数据分析工作流程中的优势

数据独立采集 (DIA) 已成为基于 MS 的蛋白质组学的成熟方法。然而，分析此类数据的选项列表相当广泛，并且光谱库的使用已成为 DIA 数据分析中的一个重要因素。更具体地说，计算机预测库的使用正在引起越来越多的兴趣。通过在恒定的酵母胰蛋白酶消化背景中使用人类标准蛋白 (UPS2) 的差异掺入，我们评估了在数据 DIA 数据分析工作流程中使用计算机预测文库与更成熟的方法相比的灵敏度、精确度和准确度工作流程。三种常用的 DIA 软件工具 DIA-NN、EncyclopeDIA 和 Spectronaut 分别在谱库模式和无谱库模式下进行了测试。在谱库模式下，我们使用独立的谱库预测工具 PROSIT 和 MS2PIP 以及 DeepLC，紧接着基于经典的基于数据依赖采集 (DDA) 的谱库。我们总共对 DIA 的 12 个计算工作流程进行了基准测试。我们的比较表明，DIA-NN 达到了最高的灵敏度，同时在无文库模式或使用计算机预测文库中保持了再现性和准确性水平的良好折衷，这表明使用计算机预测文库具有普遍的好处。