Autoprot: a modular package for processing, analysis and visualization of mass spectrometry proteomics data in Python

The rising complexity tandem mass spectrometry (MS/MS)-based proteomics data sets require standardized and reliable data analysis workflows. In this regard, scripted languages such as Python and R provide documentation of the analysis and, when combined with interactive notebooks such as Jupyter, interactive data inspection. Despite its central role for machine learning, there is only a limited repertoire of Python software available for the analysis of MS/MS data generated in data-dependent acquisition mode. In contrast, several specialised analysis tools have been developed, most prominently, in the R language. To leverage the power of both languages in data analysis, proficiency in Python and R is often required. However, this increases the hurdle for scripted data analysis to be widely adopted.

We developed autoprot, a Python module for analysing proteomics search results generated with the MaxQuant software. Autoprot offers functionalities for common data processing tasks such as initial filtering, normalization, and statistical analysis all based on the popular pandas dataframe object. Moreover, it provides an interface to specialised functions so far only available in R thereby increasing the robustness of the analysis without reinventing the wheel.

Furthermore, it generates dynamic javascript-based charts that can be integrated into interactive web applications. We show the application of autoprot using publicly available MS datasets, highlight functions of the submodules for data preprocessing, analysis and visualisation and showcase interactive plots generated with the software. Autoprot is written with collaboration and code customisation in mind using best practices for code writing as well as automatically build documentation and continuous testing via GitHub actions. In summary, autoprot provides standardised, fast, and reliable proteomics data analysis while ensuring a high customisability needed to tailor the analysis pipeline to specific experimental strategies.