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

Abstract

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.

QuantUMS: uncertainty minimisation enables confident quantification in proteomics

“…These include selected and parallel reaction monitoring as well as other experiments that involve recording multiplexed MS/MS spectra, and application of QuantUMS to these can be explored in a follow-up work. We further envision significant potential for future improvements in quantitative proteomics to be achieved by integrating QuantUMS with downstream statistical analysis approaches, such as MSStats 43 or Triqler 44 , to enable biological inference that is fully aware of all kinds of uncertainty, missingness and normalisation issues in the raw proteomics data.…”

“…These include deconvolution of spectra 38 , selection of peptide fragment ions based on the signal quality 21,26,39 , as well as protein quantification through aggregation of multiple parallel sources of quantitative information, such as peptide-level MaxLFQ for DDA 40 and fragment-level MaxLFQ for DIA 41 or directLFQ 42 . Advanced methods for error control and missing data handling have also been developed for statistical analysis of proteomics data, as discussed and benchmarked recently 43,44 . However, while peptide identification error rates are well controlled by statistically-justified target-decoy competition methods, quantification errors are currently impossible to estimate.…”

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

“…However, key challenges still remain in this process, encompassing, but not limited to, multi-run alignment ( 117 , 219 , 220 , 221 ), interference removal or peak correction and selection ( 57 , 84 , 119 , 222 , 223 ), integration of MS1 and MS2 signals ( 41 , 223 , 224 ), and high-level ( i.e. , peptide or protein) quantification inference ( 225 , 226 , 227 , 228 ). Furthermore, beyond the widely adopted label-free methods, a notable direction is isotope labeling-based quantification for DIA data, which has received growing interest ( 178 , 184 , 229 , 230 , 231 , 232 ).…”