ArticleJournal of proteome research2021
Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration.
Article in Journal of proteome research, 2021. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A Bayesian framework for multivariate differential analysis.PLoS computational biology · 2026Article
- Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry.Journal of proteome research · 2023Article
- Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Challenges and Opportunities for Bayesian Statistics in Proteomics.Journal of proteome research · 2022Review
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Authors and funding
2 authors.
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Abstract
Error estimation for differential protein quantification by label-free shotgun proteomics is challenging due to the multitude of error sources, each contributing uncertainty to the final results. We have previously designed a Bayesian model, Triqler, to combine such error terms into one combined quantification error. Here we present an interface for Triqler that takes MaxQuant results as input, allowing quick reanalysis of already processed data. We demonstrate that Triqler outperforms the original processing for a large set of both engineered and clinical/biological relevant data sets. Triqler and its interface to MaxQuant are available as a Python module under an Apache 2.0 license from https://pypi.org/project/triqler/.
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