Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration

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/.

Automated summary

Error estimation for differential protein quantification is challenging due to multiple error sources, but Triqler model combines them into one quantification error. The interface with MaxQuant allows quick reanalysis of processed data, showing superior performance in various data sets. Triqler and its interface are available as a Python module under Apache 2.0 license.