Data-independent acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data-dependent acquisition (DDA) libraries for deep peptide-centric detection. Here, it is shown that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter-laboratory comparison.

中文翻译：

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使用预测的光谱库删除DIA的隐藏数据依赖性。

不依赖数据的采集（DIA）生成全面而复杂的质谱数据，这意味着必须使用依赖数据的采集（DDA）库进行以肽为中心的深层检测。在这里，表明可以通过将预测的片段强度和保留时间与窄窗口DIA相结合来从这种依赖关系中恢复DIA。这消除了库构建中的差异，并省略了随机抽样，最终使DIA工作流具有完全确定性。特别是对于临床蛋白质组学而言，这有可能促进实验室间的比较。