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Click here for The Kinase Library Web Tool

   

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The Kinase Library is a comprehensive Python package for analyzing phosphoproteomics data, focusing on kinase-substrate relationships. It provides tools for kinase prediction, enrichment analysis, and visualization, enabling researchers to gain insights into kinase activities and signaling pathways from phosphoproteomics datasets.

Features

• Sequence Retrieval: Build the SITE_+/-7_AA window from a protein identifier and a site position, for datasets that arrive without a sequence column.
• Kinase Prediction: Predict potential kinases responsible for phosphorylation sites using a built-in kinase-substrate prediction algorithm.
• Enrichment Analysis: Perform kinase enrichment analysis using binary enrichment or differential phosphorylation analysis.
• Motif Enrichment Analysis (MEA): Identify kinases potentially regulated in your dataset using MEA with the GSEA algorithm.
• Visualization: Generate volcano plots, bubble maps, and other visualizations to interpret enrichment results.
• Downstream Substrate Identification: Explore putative downstream substrates of enriched kinases.

Installation

You can install the package via pip:

pip install kinase-library

Getting Started

The Kinase Library package offers several tools for analyzing kinase phosphorylation sites. Below are some basic examples to help you get started. Please refer to Notebooks for more comprehensive usage.

Data Updates

Citations

Please cite the following papers when using this package:

For the serine/threonine kinome:

Johnson, J. L., Yaron, T. M., Huntsman, E. M., Kerelsky, A., Song, J., Regev, A., ... & Cantley, L. C. (2023). An atlas of substrate specificities for the human serine/threonine kinome. Nature, 613(7945), 759-766. https://doi.org/10.1074/mcp.TIR118.000943

For the tyrosine kinome:

Yaron-Barir, T. M., Joughin, B. A., Huntsman, E. M., Kerelsky, A., Cizin, D. M., Cohen, B. M., ... & Johnson, J. L. (2024). The intrinsic substrate specificity of the human tyrosine kinome. Nature, 1-8. https://doi.org/10.1038/s41586-024-07407-y

License

This package is distributed under the Creative Commons License. See LICENSE for more information.