Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors
Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors
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DOI：
10.1021/acs.jproteome.1c00640
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发表时间：
2021-12-03
影响因子：
4.4
通讯作者：
Guo, Tiannan
中科院分区：
文献类型：
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作者：
Ge, Weigang;Liang, Xiao;Guo, Tiannan
从数据无关的采集质谱 (DIA-MS) 数据中进行有效的肽和蛋白质鉴定通常依赖于具有合适大小的项目特定谱库。在这里，我们描述了 subLib，一种基于综合光谱库针对特定 DIA 数据集优化光谱库的计算策略，需要对 DIA 数据集进行初步分析。与泛人类文库策略相比，subLib 在 6 个结直肠肿瘤样本的测试数据集中实现了肽前体鉴定增加 41.2%，蛋白组鉴定增加 35.6%。我们还将这一策略应用于来自 15 个肿瘤数据集的 389 个癌症样本：观察到肽前体鉴定增加了 39.2%，蛋白质组鉴定增加了 19.0%。因此，我们的谱库大小优化策略成功地证明可以加深 DIA-MS 数据的蛋白质组覆盖范围。
Efficient peptide and protein identifications from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on a project-specific spectral library with a suitable size. Here, we describe subLib, a computational strategy for optimizing the spectral library for a specific DIA data set based on a comprehensive spectral library, requiring the preliminary analysis of the DIA data set. Compared with the pan-human library strategy, subLib achieved a 41.2% increase in peptide precursor identifications and a 35.6% increase in protein group identifications in a test data set of six colorectal tumor samples. We also applied this strategy to 389 carcinoma samples from 15 tumor data sets: up to a 39.2% increase in peptide precursor identifications and a 19.0% increase in protein group identifications were observed. Our strategy for spectral library size optimization thus successfully proved to deepen the proteome coverages of DIA-MS data.