Year: 2026
Type: preprint
Abstract: Peptide microarray technologies such as PamDx 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 and compound/drug screening. We developed pyKinaXe, a Python package for automated end-to-end analysis of PamChip ® data, integrating robust image processing, kinase analysis, and pathway enrichment into a single one-click pipeline. We validated pyKinaXe on two independent datasets against existing peptide-microarray-based kinase activity inference tools, demonstrating qualitative agreement and, at the same time, drastic speed-up (6-fold for image processing and 42-fold for statistical kinase inference). pyKinaXe addresses the key limitations of existing tools (slow inference, fragmented workflows, and poor usability), enabling fast and robust analysis, and facilitating high-throughput experiments and large-scale kinome profiling. 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 are available at Mendeley Data (doi: 10.17632/ynp7f92n47.1). pyKinaXe’s user-friendly web-based interface can be accessed at https://pykinaxe.github.io/home .
Institutions: Hasso Plattner Institute, Paul Ehrlich Institut
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funder: Hasso Plattner Foundation
OpenAlex ID: W7161292745