niekwit/damid-seq

DamMapper: Snakemake workflow for DamID-Seq analysis

Overview

Latest release: v0.6.0, Last update: 2026-05-18

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

Quality control: linting: failed formatting: passed

Topics: bioinformatics-pipeline damid snakemake-workflow

Wrappers: bio/bedtools/sort bio/bowtie2/align bio/bowtie2/build bio/fastqc bio/multiqc bio/samtools/faidx bio/samtools/index bio/samtools/sort 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/damid-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 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 parameters

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

Parameter

Type

Description

Required

Default

genome

string

Ensembl genome

yes

ensembl_genome_build

integer

Ensembl genome build

yes

fusion_genes

yes

. genes

string

Genes from these proteins will be masked from the fasta file

. feature_to_mask

string

Feature to mask from the fasta file (exon or gene)

damidseq_pipeline

yes

. normalization

string

. binsize

integer

. extra

string

quantile_normalisation

. apply

boolean

. extra

string

deeptools

yes

. bamCoverage

. . binSize

integer

. . normalizeUsing

string

. . extra

string

. matrix

. . mode

string

. . referencePoint

string

. . regionBodyLength

integer

. . upstream

integer

. . downstream

integer

. . binSize

integer

. . averageTypeBins

string

. . regionsFileName

string

. . no_whole_genome

boolean

. . extra

string

. plotHeatmap

. . interpolationMethod

string

. . plotType

string

. . colorMap

string

. . alpha

number

. . extra

string

peak_calling_perl

yes

. run

boolean

. iterations

integer

. fdr

number

. fraction

number

. min_count

integer

. min_quantile

number

. step

number

. unified_peaks

string

. extra

string

peak_calling_macs3

yes

. run

boolean

. mode

string

. qvalue

number

. broad-cutoff

number

consensus_peaks

yes

. max_size

integer

. extend_by

integer

. keep

integer

. enrichment_analysis

. . run

boolean

. . dbs

array

. . terms

integer

resources

yes

. trim

. . cpu

integer

yes

. . time

integer

yes

. fastqc

. . cpu

integer

yes

. . time

integer

yes

. damid

. . cpu

integer

yes

. . time

integer

yes

. index

. . cpu

integer

yes

. . time

integer

yes

. deeptools

. . cpu

integer

yes

. . time

integer

yes

. plotting

. . cpu

integer

yes

. . time

integer

yes

Linting and formatting

Linting results
1Workflow version: v0.6.0
2Wrapper version: v5.8.3
3No validator found for JSON Schema version identifier 'http://json-schema.org/draft-06/schema#'
4Defaulting to validator for JSON Schema version 'https://json-schema.org/draft/2020-12/schema'
5Note that schema file may not be validated correctly.
6AssertionError in file "/tmp/tmpfmwx4hht/niekwit-damid-seq-c072bc2/workflow/scripts/general_functions.smk", line 358:
7No fastq files found...
8  File "/tmp/tmpfmwx4hht/niekwit-damid-seq-c072bc2/workflow/Snakefile", line 35, in <module>
9  File "/tmp/tmpfmwx4hht/niekwit-damid-seq-c072bc2/workflow/scripts/general_functions.smk", line 358, in paired_end
Formatting results
All tests passed!