Getting Started

Tutorial

A tutorial is available with a small(ish) dataset where biologically meaningful results can be produced. This can help get an understanding of a good workflow to use different modules. You can also follow along with your own data and just skip analyses you don't want. I recommend at least looking at this to get an idea of the scope of the pipeline as well as to see a few tips. If you prefer to just jump in instead, below describes how to get a new project up and running.

Requirements

This pipeline can be run on Linux systems with Conda and Apptainer/Singularity installed. All other dependencies will be handled with the workflow, and thus, sufficient storage space is needed for these installations (~10GB, though this is only when using all features of the pipeline, if you are not using any of the mapping module, it will be more like 3-5GB). It can be run on a local workstation with sufficient resources and storage space (dataset dependent), but is aimed at execution on high performance computing systems with job queuing systems.

Data-wise, you'll need a reference genome (uncompressed) and some sequencing data for your samples. The latter can be either raw FASTQ files, BAM alignments to the reference, or SRA accession numbers for already published FASTQ files.

Deploying the workflow

The pipeline can be deployed in two ways: (1) using Snakedeploy which will deploy the pipeline as a module (recommended); (2) clone the repository at the version/branch you prefer (recommended if you will need to change workflow code beyond what is possible with module definitions). If you are curious how modularization works in Snakemake, take a look at the docs.

Both methods require a Snakemake environment to run the pipeline in.

Preparing the environment

First, create an environment for Snakemake, including Snakedeploy if you intend to deploy that way:

conda create -c conda-forge -c bioconda --name popglen snakemake snakedeploy

If you already have a Snakemake environment, you can use that instead. Snakemake versions >=8 are likely to work, but most testing is on 8.20. If you intend to use a job queuing system with the pipeline, be sure to install the appropriate executor plugin. Most of the testing has been with snakemake-executor-plugin-slurm, which can be installed in the environment you are running Snakemake from.

Activate the Snakemake environment:

conda activate snakemake

Option 1. Deploying with Snakedeploy

Make your working directory:

mkdir -p /path/to/work-dir
cd /path/to/work-dir

And deploy the workflow, using the tag for the version you want to deploy:

snakedeploy deploy-workflow https://github.com/zjnolen/PopGLen . --tag v0.4.1

This will generate a simple Snakefile in a workflow folder that loads the pipeline as a module. It will also download the template config.yaml, samples.tsv, and units.tsv in the config folder.

Option 2. Cloning from GitHub

Go to the folder you would like you working directory to be created in and clone the GitHub repo:

git clone https://github.com/zjnolen/PopGLen.git

If you would like, you can change the name of the directory:

mv PopGLen work-dir-name

Move into the working directory (PopGLen or work-dir-name if you changed it) and checkout the version you would like to use:

git checkout v0.4.1

This can also be used to checkout specific branches or commits.

Configuring the workflow

Now you are ready to configure the workflow, see the documentation for that here.