Data analysis for paper: Protein Complex Similarity based on Weisfeiler-Lehman labeling

This Snakemake workflow automatically generates all results and plots from the paper.

Usage

Step 1: Setup system

Requirements

Any 64-bit Linux installation with GLIBC 2.5 or newer (i.e. any Linux distribution that is newer than CentOS 6). Gurobi is required for solving the binary linear program. An environment variable $GUROBI_HOME must point to the root folder of the gurobi installation. A Gurobi licence key must be installed.

Variant a: Installing Miniconda on your system

If you are on a Linux system with GLIBC 2.5 or newer (i.e. any Linux distribution that is newer than CentOS 6), you can simply install Miniconda3 with

curl -o /tmp/miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh && bash /tmp/miniconda.sh

Make sure to answer yes to the question whether your PATH variable shall be modified. Afterwards, open a new shell/terminal.

Variant b: Use an existing Miniconda installation

If you want to use an existing Miniconda installation, please be aware that this is only possible if it uses Python 3 by default. You can check this via

python --version

Step 2: Update to conda >=4.4

If you have an older conda version, please update to conda >=4.4 at least via

conda install conda=4.4

Step 3: Setup Bioconda channel

Setup Bioconda with

conda config --add channels defaults
conda config --add channels conda-forge
conda config --add channels bioconda

Step 4: Install Snakemake

Install bioconda-utils and Snakemake >=4.6.0 with

conda install snakemake

If you already have an older version of Snakemake, please make sure it is updated to >=4.6.0.

Step 5: Download the workflow

First, create a working directory:

mkdir complex-similarity-workflow
cd complex-similarity-workflow

Then, download the workflow archive from https://doi.org/10.5281/zenodo.1178084 and unpack it with

tar -xf workflow.tar.gz

Step 6: Run the workflow

Execute the analysis workflow with Snakemake

snakemake --use-conda --cores $CORES

With $CORES being set to a reasonable value for your system. Note that it took a few hours with 64 cores. Results can be found in the folder plots/.