Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology - KTH, Solna 17121, Sweden.
Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising 85354, Germany.
J Proteome Res. 2023 Apr 7;22(4):1359-1366. doi: 10.1021/acs.jproteome.2c00607. Epub 2023 Mar 29.
A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrometer analyzes the proteolytic peptides of a protein rather than the proteins themselves. We have previously designed a Bayesian hierarchical probabilistic model, Triqler, for combining peptide identification and quantification errors into probabilities of proteins being differentially abundant. However, the model was developed for data from data-dependent acquisition. Here, we show that Triqler is also compatible with data-independent acquisition data after applying minor alterations for the missing value distribution. Furthermore, we find that it has better performance than a set of compared state-of-the-art protein summarization tools when evaluated on data-independent acquisition data.
当处理来自定量鸟枪法蛋白质组学实验的数据时，一个常见的目标或子目标是列出在检查的实验条件下差异丰度的蛋白质列表。不幸的是，获得这样一个列表是一个具有挑战性的过程，因为质谱仪分析的是蛋白质的肽段，而不是蛋白质本身。我们之前设计了一个贝叶斯分层概率模型 Triqler，用于将肽鉴定和定量错误组合成蛋白质差异丰度的概率。然而，该模型是为依赖于数据的获取数据开发的。在这里，我们展示了 Triqler 在应用了一些小的改变以适应缺失值分布之后，也可以与独立于数据的获取数据兼容。此外，我们发现，在独立于数据的获取数据上进行评估时，它的性能优于一组比较先进的蛋白质汇总工具。
