niekwit/methyl-seq

Snakemake workflow for EM-seq / bisulfite sequencing (WGBS) DNA methylation analysis

Overview

Latest release: v0.6.0, Last update: 2026-09-24

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=niekwit/methyl-seq

Quality control: linting: failed formatting: passed

Topics: bioinformatics bisulfite-sequencing dna-methylation em-seq snakemake snakemake-workflow wgbs

Wrappers: bio/fastqc bio/multiqc bio/seqkit bio/trim_galore/pe bio/trim_galore/se

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --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

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

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/niekwit/methyl-seq . --tag v0.6.0

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 apptainer/singularity, use

snakemake --cores all --sdm apptainer

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

snakemake --cores all --sdm conda apptainer

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.

Workflow overview

This workflow is a best-practice workflow for <detailed description>. The workflow is built using snakemake and consists of the following steps:

1. Download genome reference from NCBI

2. Validate downloaded genome (python script)

3. Simulate short read sequencing data on the fly (dwgsim)

4. Check quality of input read data (FastQC)

5. Collect statistics from tool output (MultiQC)

Running the workflow

Input data

This template workflow creates artificial sequencing data in *.fastq.gz format. It does not contain actual input data. The simulated input files are nevertheless created based on a mandatory table linked in the config.yaml file (default: .test/samples.tsv). The sample sheet has the following layout:

sample	condition	replicate	read1	read2
sample1	wild_type	1	sample1.bwa.read1.fastq.gz	sample1.bwa.read2.fastq.gz
sample2	wild_type	2	sample2.bwa.read1.fastq.gz	sample2.bwa.read2.fastq.gz

Parameters

This table lists all parameters that can be used to run the workflow.

parameter	type	details	default
samplesheet
path	str	path to samplesheet, mandatory	“config/samples.tsv”
get_genome
ncbi_ftp	str	link to a genome on NCBI’s FTP server	link to S. cerevisiae genome
simulate_reads
read_length	num	length of target reads in bp	100
read_number	num	number of total reads to be simulated	10000

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter	Type	Description	Required	Default
genome	string	Genome assembly to use	yes
ensembl_genome_build	integer	Ensembl genome build version	yes
temp_dir	string	Temporary directory for intermediate files
trim_galore_args	string	Additional arguments to pass to Trim Galore
bismark		Extra Bismark arguments for each Bismark sub command	yes
. align	string	Extra arguments for Bismark alignment	yes
. deduplicate	string	Extra arguments for Bismark deduplication	yes
. extract	string	Extra arguments for Bismark methylation extraction	yes
. coverage	string	Extra arguments for Bismark coverage/cytosine report generation (bismark_methylation_extractor –cytosine_report, run as part of the same methylation_extraction step as “extract” – Bismark has no separate coverage2cytosine invocation in this workflow)	yes
deeptools		Deeptools parameters	yes
. bigwig_summary			yes
. . binSize	integer	Bin size for bigwig summary	yes
. . extra	string	Extra arguments for bigwig summary	yes
. plotPCA			yes
. . extra	string	Extra arguments for plotPCA	yes
boxplot		% CpG methylation boxplots for different genomic regions	yes
. plot	boolean	Whether to generate boxplots	yes
. cpg_n	integer	Number of CpGs per probe	yes
. min_reads	integer	Minimum reads covering a CpG to keep	yes
. regions		Optional extra custom genomic regions (name -> BED file path) to plot boxplots for, on top of the automatically generated standard regions
DMR		Differentially methylated region (DMR) calling via methylKit	yes
. run	boolean	Whether to run DMR calling	yes
. reference_condition	string	Condition to compare every other condition against; must match config/samples.csv	yes
. tile_size	integer	Genome tile size (bp) tested for differential methylation	yes
. step_size	integer	Tile step size (bp); overlapping tiles are possible if less than tile_size	yes
. min_per_group	integer	Minimum samples covered per group for a tile to be tested	yes
. difference_threshold	number	Minimum absolute methylation difference (percentage points) for a tile to be called a DMR	yes
. qvalue_threshold	number	Maximum q-value for a tile to be called a DMR	yes
. profile		computeMatrix reference-point / plotHeatmap / plotProfile settings for CpG methylation centred on each DMR’s midpoint, using results/bigwig/{condition}.bw as signal, run separately for hypo- and hypermethylated DMRs. Produces results/plots/dmrs/{status}_heatmap.pdf and results/plots/dmrs/{status}_profile.pdf.	yes
. . upstream	integer	Region upstream of each DMR’s centre to include (bp)	yes
. . downstream	integer	Region downstream of each DMR’s centre to include (bp)	yes
. . binSize	integer	Bin size (bp) for signal averaging	yes
. . extra	string	Extra arguments passed to computeMatrix	yes

Linting and formatting

Linting results

/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/google/auth/transport/grpc.py:43: FutureWarning: grpcio < 1.83.0 does not support Post-Quantum Cryptography (PQC). Support for non-PQC environments is deprecated. In April 2027, google-auth will raise its minimum requirements to enforce grpcio >= 1.83.0. For more details on Google Cloud's post-quantum security migration, visit: https://cloud.google.com/security/resources/post-quantum-cryptography
  warnings.warn(
ValueError in file "/tmp/tmpprnjdz2l/niekwit-methyl-seq-8759d71/workflow/rules/common.smk", line 83:
No FASTQ (*.fastq.gz) files found in 'reads/' directory.
  File "/tmp/tmpprnjdz2l/niekwit-methyl-seq-8759d71/workflow/Snakefile", line 18, in <module>
  File "/tmp/tmpprnjdz2l/niekwit-methyl-seq-8759d71/workflow/rules/common.smk", line 83, in paired_end

Formatting results

All tests passed!