工作流程
计算机科学
质谱法
串联质谱法
数据采集
碎片(计算)
错误发现率
数据挖掘
化学
色谱法
数据库
生物化学
基因
操作系统
作者
Lilian R. Heil,William E. Fondrie,Christopher D. McGann,Alexander Federation,William Stafford Noble,Michael J. MacCoss,Uri Keich
标识
DOI:10.1021/acs.jproteome.1c00895
摘要
Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI