ArticleJournal of proteome research2023
Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry.
Article in Journal of proteome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Accurate quantification in proteomics with QuantUMS.Nature biotechnology · 2026Article
- A shrinkage-based statistical method for testing group mean differences in quantitative bottom-up proteomics.BMC bioinformatics · 2025Article
- Improved Detection of Differentially Abundant Proteins through FDR-Control of Peptide-Identity-Propagation.Journal of proteome research · 2025Article
- Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach.Bioinformatics (Oxford, England) · 2025Article
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3 authors.
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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.
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