{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Virus-Host Interplay Lab, Digital Health Cluster, Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert Strasse 2-3, 14482 Potsdam, Brandenb"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "D. Wuttke"
    },
    {
      "name": "E. Hildt"
    },
    {
      "affs": [
        0
      ],
      "name": "P. V. Kolesnichenko"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4f9d2cek5c8xb.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.05.12.724658",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.05.12.724658v2.full",
  "id": "4f9d2cek5c8xb",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.05.12.724658v2",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Advanced Biosensing Techniques and Applications"
    ]
  ],
  "openalex": "W7161292745",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.05.12.724658v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4f9d2cek5c8xb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:38Z",
      "native_id": "10.64898/2026.05.12.724658",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.05.12.724658v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "pyKinaXe: a fast and robust turnkey kinase activity profiler",
  "title_text": "pyKinaXe: a fast and robust turnkey kinase activity profiler",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:38Z",
  "url": "https://research.draconitic.com/paper/4f9d2cek5c8xb",
  "venue": "bioRxiv",
  "versions": []
}