Kinex - Kinome Exploration Tool

Kinex is a Python package for inferring causal kinases from phosphoproteomics data.

Paper: Kinex infers causal kinases from phosphoproteomics data. https://doi.org/10.1101/2023.11.23.568445

Main Features

• Substrate Sequence Scoring
• Causal Kinases Inference
• Comparison with Drug Collection

Requirements

• conda
• Python 3.11

Installation

From Conda

# Create and activate your conda environment
conda create --name kinex
conda activate kinex

# Install kinex package
conda install -c bioconda kinex

From Source

# Create and activate a Python 3.11 conda environment 
conda create --name kinex
conda activate kinex
conda install python=3.11

# Download the package:
git clone [email protected]:bedapub/kinex.git
cd kinex

# Install the package
pip install .

Quick Start

1. Import Package and Create Kinex Object

from kinex import Kinex
import pandas as pd

Create Kinex Object

1. With Predefined Matrices:

   kinex = Kinex()

2. With Your Custom Matrices:

   kinex = Kinex(scoring_matrix_ser_thr=pd.read_csv('path_to_ser_thr_matrix.csv'), scoring_matrix_tyr=pd.read_csv('path_to_tyr_matrix.csv'))

Predefined matrices can be found here:

• Scoring Matrix for Serine/Threonine
• Scoring Matrix for Tyrosine

2. Score a Sequence

sequence = "FVKQKAY*QSPQKQ"
res = kinex.get_score(sequence)

3. Enrichment Analysis

enrich = kinex.get_enrichment(input_sites, fc_threshold=1.5, phospho_priming=False, favorability=True, method="max")

enrich.ser_thr.plot()
enrich.tyr.plot()

Documentation

You can find detailed documentation describing every feature of the package with examples and tutorials here.