Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry

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.

Automated summary

A common goal in quantitative shotgun proteomics experiments is to identify proteins that show differential abundance under specific experimental conditions. However, this is challenging as mass spectrometry analyzes protein fragments rather than intact proteins. In this study, we demonstrate that a Bayesian hierarchical probabilistic model called Triqler can be adapted for data-independent acquisition (DIA) data after making minor changes to account for missing values. Furthermore, we show that Triqler outperforms other state-of-the-art protein summarization tools when evaluated on DIA data.