pyKinaXe: a fast and robust turnkey kinase activity profiler with high resolution

MotivationPeptide microarray technologies such as PamGenes enable direct measurement of peptide phosphorylation by upstream kinases, yet extraction of kinases from raw data depends on proprietary software or separate open-source alternatives delivering time-consuming processing across a variety of different steps, limiting throughput for experimental large-scale kinome generation in clinical and research settings. ResultsWe developed pyKinaXe, a Python package for automated end-to-end analysis of PamChip(R) data, integrating robust image processing, quantification of phosphorylation kinetics, multi-database substrate-kinase mapping, and upstream kinase analysis into a single one-click pipeline. Validation on a selected published benchmark dataset recovered 76-89% of the signaling pathways for previously reported significantly deregulated kinases. Processing time was reduced on the same data from over 30 minutes to[~] 25 seconds, leading to a 75-fold speed increase compared to other open-source alternatives. Thus, pyKinaXe addresses the key limitations of existing peptide-microarray-based kinase activity inference tools (slow inference, fragmented workflows, and poor usability) enabling fast and robust analysis, and facilitating high-throughput experiments and large-scale kinome profiling. Availability and implementationpyKinaXe is implemented in Python 3.13 and distributed under the Apache 2.0 License. Source code, documentation, and installation instructions are freely available at https://github.com/pykinaxe/pyKinaXe. The benchmark data is available at Mendeley Data (doi: 10.17632/ynp7f92n47.1). A pyKinaXes user-friendly web-based interface can be accessed at https://pykinaxe.github.io/home.