tmweiskittel/tmw_analysis_emseq

None

Overview

Latest release: None, Last update: 2026-09-08

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=tmweiskittel/tmw_analysis_emseq

Quality control: linting: failed formatting: failed

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/tmweiskittel/tmw_analysis_emseq . --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 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

Workflow parameters

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

Parameter

Type

Description

Required

Default

sample_sheet

string

path to sample sheet, mandatory

yes

config/samples.tsv

get_genome

yes

. ncbi_ftp

string

URL for genome retrieval from NCBI FTP server

yes

simulate_reads

yes

. read_length

integer

length of target reads in bp

yes

100

. read_number

integer

number of total reads to be simulated

yes

10000

Linting and formatting

Linting results
 1/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/google/auth/transport/grpc.py:44: FutureWarning: grpcio < 1.83.0 does not support Post-Quantum Cryptography (PQC). Support for non-PQC environments is deprecated. In October 2026, 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
 2  warnings.warn(
 3Lints for snakefile /tmp/tmp1x6w24m9/workflow/Snakefile:
 4    * Mixed rules and functions in same snakefile.:
 5      Small one-liner functions used only once should be defined as lambda
 6      expressions. Other functions should be collected in a common module, e.g.
 7      'rules/common.smk'. This makes the workflow steps more readable.
 8      Also see:
 9      https://snakemake.readthedocs.io/en/latest/snakefiles/modularization.html#includes
10
11Lints for rule download_methylkit_raw (line 1, /tmp/tmp1x6w24m9/workflow/rules/download_differential_methylation.smk):
12    * Specify a conda environment or container for each rule.:
13      This way, the used software for each specific step is documented, and the
14      workflow can be executed on any machine without prerequisites.
15      Also see:
16      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
17      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
18
19Lints for rule make_single_methylkit_tabix_db (line 23, /tmp/tmp1x6w24m9/workflow/rules/download_differential_methylation.smk):
20    * Param out_dir is a prefix of input or output file but hardcoded:
21      If this is meant to represent a file path prefix, it will fail when
22      running workflow in environments without a shared filesystem. Instead,
23      provide a function that infers the appropriate prefix from the input or
24      output file, e.g.: lambda w, input: os.path.splitext(input[0])[0]
25      Also see:
26      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
27      https://snakemake.readthedocs.io/en/stable/tutorial/advanced.html#tutorial-input-functions
28
29Lints for rule download_gencode_gtf (line 1, /tmp/tmp1x6w24m9/workflow/rules/setup_differential_methylation.smk):
30    * No log directive defined:
31      Without a log directive, all output will be printed to the terminal. In
32      distributed environments, this means that errors are harder to discover.
33      In local environments, output of concurrent jobs will be mixed and become
34      unreadable.
35      Also see:
36      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
37    * Specify a conda environment or container for each rule.:
38      This way, the used software for each specific step is documented, and the
39      workflow can be executed on any machine without prerequisites.
40      Also see:
41      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
42      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
43
44Lints for rule download_qc_summary (line 1, /tmp/tmp1x6w24m9/workflow/rules/qc_summary.smk):
45    * Specify a conda environment or container for each rule.:
46      This way, the used software for each specific step is documented, and the
47      workflow can be executed on any machine without prerequisites.
48      Also see:
49      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
50      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
51
52Lints for rule upload_differential_methylation_results (line 1, /tmp/tmp1x6w24m9/workflow/rules/cloud_upload.smk):
53    * Specify a conda environment or container for each rule.:
54      This way, the used software for each specific step is documented, and the
55      workflow can be executed on any machine without prerequisites.
56      Also see:
57      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
58      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
59    * Shell command directly uses variable D_OUT from outside of the rule:
60      It is recommended to pass all files as input and output, and non-file
61      parameters via the params directive. Otherwise, provenance tracking is
62      less accurate.
63      Also see:
64      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
65
66Lints for rule download_ichor_bam (line 1, /tmp/tmp1x6w24m9/workflow/rules/ichor_cna.smk):
67    * Specify a conda environment or container for each rule.:
68      This way, the used software for each specific step is documented, and the
69      workflow can be executed on any machine without prerequisites.
70      Also see:
71      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
72      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
73
74Lints for rule ichor_run_sample (line 50, /tmp/tmp1x6w24m9/workflow/rules/ichor_cna.smk):
75    * Param out_dir is a prefix of input or output file but hardcoded:
76      If this is meant to represent a file path prefix, it will fail when
77      running workflow in environments without a shared filesystem. Instead,
78      provide a function that infers the appropriate prefix from the input or
79      output file, e.g.: lambda w, input: os.path.splitext(input[0])[0]
80      Also see:
81      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
82      https://snakemake.readthedocs.io/en/stable/tutorial/advanced.html#tutorial-input-functions
83
84Lints for rule upload_ichor_results (line 114, /tmp/tmp1x6w24m9/workflow/rules/ichor_cna.smk):
85    * Specify a conda environment or container for each rule.:
86      This way, the used software for each specific step is documented, and the
87      workflow can be executed on any machine without prerequisites.
88      Also see:
89      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
90      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
Formatting results
 1[DEBUG] 
 2[DEBUG] In file "/tmp/tmp1x6w24m9/workflow/Snakefile":  Formatted content is different from original
 3[DEBUG] 
 4[DEBUG] In file "/tmp/tmp1x6w24m9/workflow/rules/cloud_upload.smk":  Formatted content is different from original
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 6[DEBUG] In file "/tmp/tmp1x6w24m9/workflow/rules/setup_differential_methylation.smk":  Formatted content is different from original
 7[DEBUG] 
 8[DEBUG] In file "/tmp/tmp1x6w24m9/workflow/rules/qc_summary.smk":  Formatted content is different from original
 9[DEBUG] 
10[DEBUG] In file "/tmp/tmp1x6w24m9/workflow/rules/ichor_cna.smk":  Formatted content is different from original
11[DEBUG] 
12[DEBUG] In file "/tmp/tmp1x6w24m9/workflow/rules/download_differential_methylation.smk":  Formatted content is different from original
13[DEBUG] 
14[DEBUG] In file "/tmp/tmp1x6w24m9/workflow/rules/differential_methylation.smk":  Formatted content is different from original
15[INFO] 7 file(s) would be changed 😬
16
17snakefmt version: 0.11.5