zjnolen/PopGLen

Bioinformatics pipeline to process whole genome resequencing data and perform genotype likelihood based population genomic analyses using ANGSD and related softwares. Flexible to datasets that combine high/low coverage and historical/fresh samples.

Overview

Topics: snakemake bioinformatics bioinformatics-pipeline genotype-likelihoods population-genomics whole-genome-sequencing museomics angsd

Latest release: v0.4.1, Last update: 2025-02-11

Linting: linting: failed, Formatting:formatting: passed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

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

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

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

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow using a combination of conda and apptainer/singularity for software deployment, use

snakemake --cores all --sdm conda apptainer

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Configuration

Use the template config.yaml to customize the workflow to your dataset. You can find information about how the configuration works in the pipeline's documentation in the "Configuration" section.

Linting and formatting

Linting results

Using workflow specific profile profiles/default for setting default command line arguments.
FileNotFoundError in file /tmp/tmpcotevckz/zjnolen-PopGLen-b8e77a0/workflow/rules/common.smk, line 54:
[Errno 2] No such file or directory: 'path/to/ref.fa'
  File "/tmp/tmpcotevckz/zjnolen-PopGLen-b8e77a0/workflow/rules/common.smk", line 108, in <module>
  File "/tmp/tmpcotevckz/zjnolen-PopGLen-b8e77a0/workflow/rules/common.smk", line 54, in chunkify

Formatting results

None