pyKinaXe: a fast and robust turnkey kinase activity profiler with high resolution
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Motivation
Peptide microarray technologies such as PamGene’s 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.
Results
We developed pyKinaXe, a Python package for automated end-to-end analysis of PamChip ® 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 implementation
pyKinaXe 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 pyKinaXe’s user-friendly web-based interface can be accessed at https://pykinaxe.github.io/home .
