niekwit/methyl-seq

Snakemake workflow for bisulphite sequencing analysis

Overview

Latest release: 0.5.0, Last update: 2026-04-09

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

Quality control: linting: failed formatting: failed

Wrappers: bio/fastqc bio/multiqc 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 0.5.0

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

ensembl_genome_build

integer

Ensembl genome build version

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

. align

string

Extra arguments for Bismark alignment

. deduplicate

string

Extra arguments for Bismark deduplication

. extract

string

Extra arguments for Bismark methylation extraction

. coverage

string

Extra arguments for Bismark coverage report generation

. report

string

Extra arguments for Bismark report generation

deeptools

Deeptools parameters

. bigwig_summary

. . binSize

integer

Bin size for bigwig summary

. . extra

string

Extra arguments for bigwig summary

. plotPCA

. . extra

string

Extra arguments for plotPCA

boxplot

% CpG methylation boxplots for different genomic regions

. plot

boolean

Whether to generate boxplots

. cpg_n

integer

Number of CpGs per probe

. min_reads

integer

Minimum reads covering a CpG to keep

. regions

Genomic regions in BED format to plot boxplots for

. . CpG_islands

string

Path to BED file for CpG islands

Linting and formatting

Linting results
1ValueError in file "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/rules/common.smk", line 50:
2No FASTQ (*.fastq.gz) files found in 'reads/' directory.
3  File "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/Snakefile", line 9, in <module>
4  File "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/rules/common.smk", line 50, in paired_end
Formatting results
 1[DEBUG] 
 2[DEBUG] 
 3[DEBUG] In file "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/rules/bigwig.smk":  Formatted content is different from original
 4[DEBUG] 
 5[DEBUG] In file "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/rules/resources.smk":  Formatted content is different from original
 6[DEBUG] 
 7[DEBUG] 
 8[DEBUG] In file "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/rules/qc.smk":  Formatted content is different from original
 9[DEBUG] 
10[DEBUG] In file "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/Snakefile":  Formatted content is different from original
11[DEBUG] 
12[DEBUG] In file "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/rules/process_reads.smk":  Formatted content is different from original
13[DEBUG] 
14[DEBUG] In file "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/rules/common.smk":  Formatted content is different from original
15[DEBUG] 
16[DEBUG] In file "/tmp/tmpb1u6j19s/niekwit-methyl-seq-96a47f4/workflow/rules/process_methylation_calls.smk":  Formatted content is different from original
17[INFO] 7 file(s) would be changed 😬
18[INFO] 2 file(s) would be left unchanged 🎉
19
20snakefmt version: 0.11.5