Mass spectrometry with data-independent acquisition (DIA) is a promising method to improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory by systematically measuring all peptide precursors in a biological sample. However, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms (SNPs) and alternative site localizations in phosphoproteomics data. We report Specter (https://github.com/rpeckner-broad/Specter), an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly through comparison to a spectral library, thus circumventing the problems associated with typical fragment-correlation-based approaches. We validate the sensitivity of Specter and its performance relative to that of other methods, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

从理论上讲，通过系统地测量生物样品中的所有肽前体，具有数据独立采集（DIA）的质谱技术是提高靶向蛋白质组学和发现蛋白质组学的全面性和可重复性的一种有前途的方法。但是，在卷积光谱中区分具有相似序列的肽段所涉及的分析难题限制了其在重要情况下的适用性，例如单核苷酸多态性（SNP）的检测和磷酸化蛋白质组学数据中的替代位点定位。我们报告了Spectre（https://github.com/rpeckner-broad/Specter），这是一种开放源代码的软件工具，该工具使用线性代数直接通过与光谱库进行比较来对DIA混合光谱进行反卷积，从而规避了与典型片段相关的问题基于相关的方法。