semenko/serpent-methylation-pipeline

An efficient, documented, reproducible Snakemake methylation analysis pipeline for BS-seq and EM-seq samples, including cfDNA.

Overview

Topics: bsseq methylation pipeline snakemake emseq bisulfite bs-seq em-seq epigenetics

Latest release: None, Last update: 2025-03-04

Linting: linting: failed, Formatting:formatting: failed

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/semenko/serpent-methylation-pipeline . --tag None

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 with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

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.

To configure this workflow, currently you can edit workflow/Snakefile to specify the files you want to operate on.

General Settings

(In the future, I'll add a .yaml to specify configuration files, PR's welcome!)

Sample Definition

It is critical to define your samples correctly in data/EXPERIMENT.csv, and a working sample as data/test.csv is provided.

  • You must provide (at the least):
    • sample_id (an arbitrary name for the sample)
    • path_R1 & path_R2 (path to your paired R1/R2 .fastq.gz)
    • method (em-seq or bs-seq)
    • md5sum_R1 & md5sum_R2 (known MD5 sums of your .fastq.gz's)

All other columns are optional.

Linting and formatting

Linting results

Parsing experiment: test
Parsed 1 total sample files.
InvalidPluginException in file /tmp/tmp04hv2w9x/workflow/Snakefile, line 184:
Error loading Snakemake plugin http: The package snakemake-storage-plugin-http is not installed.

Formatting results

[DEBUG] 
[ERROR] In file "/tmp/tmp04hv2w9x/workflow/Snakefile":  NoParametersError: L311: In input definition.
[INFO] In file "/tmp/tmp04hv2w9x/workflow/Snakefile":  1 file(s) raised parsing errors 🤕

snakefmt version: 0.10.2