pyKinaXe: a fast and robust turnkey kinase activity profiler
- 1Virus-Host Interplay Lab, Digital Health Cluster, Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert Strasse 2-3, 14482 Potsdam, Brandenb
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(R) 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.
Related
- An Apoptotic Failsafe Program Involving PDGF-dependent PI 3-Kinase Activity Contributes to Plaque Composition in Atherosclerosis
- KSTAR v1.2: A faster and more and accessible KSTAR for kinase activity inference
- The Sulfated PSY Peptide Negatively Regulates Receptor Kinase Activity to Promote Growth
- ASPIRE: the Amplicon Sequencing Profiler for Investigating Respiratory Ecosystems
- NCI-Profiler: automated protein-ligand interaction detection and visualization
BibTeX
@misc{wuttke2026pykinaxe,
title = {{pyKinaXe: a fast and robust turnkey kinase activity profiler}},
author = {D. Wuttke and E. Hildt and P. V. Kolesnichenko},
howpublished = {bioRxiv},
year = {2026},
month = may,
doi = {10.64898/2026.05.12.724658},
url = {https://www.biorxiv.org/content/10.64898/2026.05.12.724658v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - pyKinaXe: a fast and robust turnkey kinase activity profiler
AU - D. Wuttke
AU - E. Hildt
AU - P. V. Kolesnichenko
PY - 2026
DA - 2026/05/12
PB - bioRxiv
DO - 10.64898/2026.05.12.724658
UR - https://www.biorxiv.org/content/10.64898/2026.05.12.724658v2
AB - 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(R) 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.
ER -