gwas-bionets2

Network-based approaches for gene discovery by leveraging GWAS data

In comparison with previous version gwas-bionets, this version is developed using Nextflow domain-specific language 2, and includes more network-based methods: Hierarchical HotNet, SigMod, Heinz, dmGWAS, LEAN and HotNet2 (you may exclude it if Hierarchical HotNet is used). Although gwas-bionets resembles a previous version of this repository, I decided not to continue in the same repo simply because the technologies are different. I want to ensure that everything is well-documented in case someone needs to reproduce it, especially in HPC environments where permissions are restricted and programs are often outdated by default. Also, I have not decided to include everything in a container because not all of the methods can be freely distributed to third parties.

Software requirements

I explain how to install all the network-based methods, and how to set the environment for running them.

Ideally, create a folder in your home directory to store all software. For example:

mkdir ~/bin

Install Java (required for installing nextflow)

Create a "java" folder in the software directory and navigate to it.

mkdir ~/bin/java
cd ~/bin/java

Download the x64 version of Java JDK as a tar.gz file from the Oracle website into your machine and decompress it. In this case is version 24, but you can check latest releases.

wget https://download.oracle.com/java/24/latest/jdk-24_linux-x64_bin.tar.gz
tar xzfv jdk-24_linux-x64_bin.tar.gz 
rm jdk-24_linux-x64_bin.tar.gz

Export the path to the bin directory of this folder into the system variable $PATH to make Java executable. Also, export the $JAVA_HOME variable indicating the root directory. Ideally, add these to ~/.bashrc to avoid repeating the process on each server connection or reboot, eg.

export PATH=/home/username/bin/java/jdk-24.0.1/bin:$PATH
export JAVA_HOME=/home/username/bin/java/jdk-24.0.1

You may need to source .bashrc file before checking installation, so type:

source ~/.bashrc

Test the installation:

java -version

You should see something like:

java version "24.0.1" 2025-04-15
Java(TM) SE Runtime Environment (build 24.0.1+9-30)
Java HotSpot(TM) 64-Bit Server VM (build 24.0.1+9-30, mixed mode, sharing)

Install Nextflow

People at sequera provide better explanations and documentation about Nextflow than I could offer. Please follow the installation steps there.

Install MAGMA

You can follow the installation instructions for MAGMA at its website (version 1.10): Multi-marker Analysis of GenoMic Annotation. According to the documentation MAGMA: is a self-contained executable and does not need to be installed.

Go to our software directory and create a 'magma' folder where the binaries will be located.

cd ~/bin
mkdir magma

Download the zip file.

curl -v "https://vu.data.surfsara.nl/index.php/s/zkKbNeNOZAhFXZB/download" -H "Accept-Encoding: zip" > magma_v1.10.zip

Descompress magma_v1.10.zip file. We will decompress to the created folder magma (unsing unzip -d option)

unzip magma_v1.10.zip -d magma

Remove the zip file.

rm magma_v1.10.zip

Test MAGMA

./magma/magma

You should see something like:

No arguments specified. Please consult manual for usage instructions.

Exiting MAGMA. Goodbye.

You can decide to include MAGMA's location into the PATH variable so it is called system-wide under your session. Otherwise, you must indicate where to find MAGMA when calling magma_calc.nf script; it has a parameter named magma for this purpose.

Install PLINK

Similarly, you can install PLINK (version 1.9) from its website: population linkage. PLINK is also self-contained executable so either you add to your path or reference the executable when using it.

Again, please locate our software directory and create a 'plink' folder where the binaries will be saved.

cd ~/bin
mkdir plink

Download the zip file

wget https://s3.amazonaws.com/plink1-assets/plink_linux_x86_64_20241022.zip

Decompress plink_linux_x86_64_20241022.zip file. We will decompress to the created folder plink (unsing unzip -d option)

unzip plink_linux_x86_64_20241022.zip -d plink

Remove the zip file.

rm plink_linux_x86_64_20241022.zip

Test PLINK

./plink/plink --version

You should see something like:

PLINK v1.9.0-b.7.7 64-bit (22 Oct 2024)

We have to include PLINK's location into our PATH variable because there is no parameter in the pipeline to reference its location. Conversely, we did include a PLINK parameter to indicate PLINK version, either 1 or 2. Then, to add plink to the environment variable, you proceed as follow:

export PATH=$PATH:/home/username/bin/plink

You can add it to your .bashrc file to make it permanent. And then, source ~/.bashrc to apply changes in your current session.

Install R and some packages (required for the methods)

If you dont't have R installed in your machine (add your superuse credentials if needed to install software), then proceed as follows (or you can check instructions from The Comprehensive R archive Network):

wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc
add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/"
apt-get -y install --no-install-recommends r-base r-base-dev

Setup the general CRAN repo.

echo 'local({
    r <- getOption("repos")
    r["CRAN"] = "https://cloud.r-project.org/"
    options(repos = r)
  })' >> /etc/R/Rprofile.site

Install Bioconductor (BiocManager), twilight and BioNet (the latter contains the necessary files to use Heinz method).

R -e "install.packages('BiocManager')"
R -e "BiocManager::install('BioNet')"
R -e "BiocManager::install('twilight')"

Install R packages, tidyverse, cowplot, igraph and gprofiler2:

R -e "install.packages(c('tidyverse', 'cowplot', 'igraph', 'gprofiler2', 'foreach'))" 

Install LEAN

As of the time of writing this README, LEANR has been removed from the CRAN repository. Therefore, a manual installation is recommended.

wget https://cran.r-project.org/src/contrib/Archive/LEANR/LEANR_1.4.9.tar.gz

And then install it using R console.

install.packages("path/to/LEANR_1.4.9.tar.gz", repos = NULL, type = "source")

Install dmGWAS

The pipeline uses dmGWAS v3.0 or EW_dmGWAS released on October 4, 2014, one can refer to this page for more information. Download it as follows:

wget https://bioinfo.uth.edu/dmGWAS/dmGWAS_3.0.tar.gz

And then install it using R console

install.packages("path/to/dmGWAS_3.0.tar.gz", repos = NULL, type = "source")

Install SigMod

You can install SigMod (version 2) from this website: Strongly Interconnected Gene MODule. It contains R scripts, then it suffices to assign the parameter sigmod_path when calling the bionets.nf script, eg. --sigmod_path="~/bin/SigMod_v2".

You change directory to our software directory bin folder:

cd ~/bin

Download the zip file and decompress it (no need to create a folder since there is a folder inside including the code and manual):

wget https://github.com/YuanlongLiu/SigMod/raw/20c561876d87a0faca632a6b93882fcffd719b17/SigMod_v2.zip
unzip SigMod_v2.zip

Change folder name

mv SigMod_v2 sigmod

Install Heinz

You have already installed it when installing the BioNet package from Bioconductor :-)

After that, we are all set!

Main Scripts

1. This script works with the raw data for splitting it if parametrized with the k parameter. The script has the needed parameters to be filled by the user, clearly, you can run each of the steps within the script separately.

bionets_construction_from_data.sh

2. This script works with the scores previously computed using a software like MAGMA for the gene P-values. Again, a k parameter greater than 1 generates k-fold solutions. As above, we conceived the script to be modified to provide the parameters.

bionets_construction_from_scores.sh