Data dependent acquisition (DDA) and data independent acquisition (DIA) are traditionally separate experimental paradigms in bottom-up proteomics. In this work, we developed a strategy combining the two experimental methods into a single LC-MS/MS run. We call the novel strategy data dependent–independent acquisition proteomics, or DDIA for short. Peptides identified from DDA scans by a conventional and robust DDA identification workflow provide useful information for interrogation of DIA scans. Deep learning based LC-MS/MS property prediction tools, developed previously, can be used repeatedly to produce spectral libraries facilitating DIA scan extraction. A complete DDIA data processing pipeline, including the modules for iRT vs RT calibration curve generation, DIA extraction classifier training, and false discovery rate control, has been developed. Compared to another spectral library-free method, DIA-Umpire, the DDIA method produced a similar number of peptide identifications, but nearly twice as many protein group identifications. The primary advantage of the DDIA method is that it requires minimal information for processing its data.

中文翻译：

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数据依赖无关采集（DDIA）蛋白质组学。

传统上，数据自相关采集（DDA）和数据独立采集（DIA）是自下而上的蛋白质组学中分离的实验范式。在这项工作中，我们制定了将两种实验方法结合到一个LC-MS / MS运行中的策略。我们称这种新颖的策略数据相关的独立采集蛋白质组学，简称DDIA。通过常规且强大的DDA识别工作流程从DDA扫描中识别出的肽为询问DIA扫描提供了有用的信息。以前开发的基于深度学习的LC-MS / MS属性预测工具可以重复使用，以生成有助于DIA扫描提取的光谱库。完整的DDIA数据处理管道，包括用于iRT和RT校准曲线生成，DIA提取分类器训练以及错误发现率控制的模块，已经被开发出来。与另一种无谱库的方法DIA-Umpire相比，DDIA方法产生的肽段鉴定次数相似，但几乎是蛋白质组鉴定次数的两倍。DDIA方法的主要优点是它需要最少的信息来处理其数据。