SnapKin

SnapKin: Protein phosphorylation site prediction for phosphoproteomic data using an ensemble deep learning approach.

Requirements

The following are the dependencies required to run the model

    python = 3.8
    tensorflow = 2.2.0
    numpy >= 1.19.4
    pandas >= 1.1.5

Useful Packages

SnapKin can be used in R and an example workflows can be found in the articles. The following R packages are used in the example R workflow:

    PhosR        : (sequence information scoring and kinase-substrate labelling)
    r-reticulate : (integrates Python into R)
    dplyr        : (dataframe manipulation)

Note. Please use the development version of PhosR on Github by installing using devtools

    install.packages('devtools')
    devtools::install_github("PYangLab/PhosR")

Conda

We recommend installing the necessary dependencies via Conda (refer to Install Conda).

Installation: Commandline

The following code snippet is for initialising and activating a Conda environment on the commandline for Tensorflow with CPU:

    conda env create -f environment.yml
    conda activate SnapKin

This installs the necessary dependencies in a new environment and activates it.

For GPU support, use environment-gpu.yml and activate SnapKin-GPU. Note. Our method for GPU support is not tested for MacOS, but CPU support is available for MacOS.

Installation: R

A helper function is included to install the appropriate conda environment in R by running the following code.

    install.packages('r-reticulate')
    SnapKin::installSnapkin(useGPU=FALSE)

For non-MacOS users, Tensorflow-GPU may be installed by using useGPU=TRUE.

Example

Please follow the Python or R work for testing on the example data:

• SnapKin Workflow in Python

• SnapKin Workflow in R