ausswal/PromEvo

PromEvo is a pipeline to analyze core, proximal promoter elements, nucleotide composition, CpG islands, DNA structural properties, Non B-DNA motifs

Overview

Latest release: v0.1.6, Last update: 2026-10-14

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=ausswal/PromEvo

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/ausswal/PromEvo . --tag v0.1.6

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.

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.

Configuration

config/config.yaml has two parts.

Key

Meaning

scripts_dir

Folder with the PromEvo stage scripts. A relative path is taken from the repository root (default scripts). Override on the command line with --config scripts_dir=/absolute/path.

stages

The 17 stages in order. Each entry has id, script, after (what must finish first; databases is the motif database setup), and optionally guard: protein (skipped automatically when Stage 01 found no real protein data) and threads.

Stage 01 is interactive: it asks for the data-retrieval mode and inputs, then writes outputs/.pipeline_data_manifest.json. Every later stage requires that file.

Finished steps are recorded as marker files in outputs/.done/. Delete outputs/.done/1.done and outputs/.done/{2..17}.done to redo Stage 01 and everything after it.

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: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
 2  warnings.warn(
 3Lints for snakefile /tmp/tmpcyj6aeol/ausswal-PromEvo-3854b36/workflow/Snakefile:
 4    * Path composition with '+' in line 6:
 5      This becomes quickly unreadable. Usually, it is better to endure some
 6      redundancy against having a more readable workflow. Hence, just repeat
 7      common prefixes. If path composition is unavoidable, use pathlib or
 8      (python >= 3.6) string formatting with f"...".
 9
10Lints for rule databases (line 150, /tmp/tmpcyj6aeol/ausswal-PromEvo-3854b36/workflow/Snakefile):
11    * Specify a conda environment or container for each rule.:
12      This way, the used software for each specific step is documented, and the
13      workflow can be executed on any machine without prerequisites.
14      Also see:
15      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
16      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
17    * Shell command directly uses variable SCRIPTS from outside of the rule:
18      It is recommended to pass all files as input and output, and non-file
19      parameters via the params directive. Otherwise, provenance tracking is
20      less accurate.
21      Also see:
22      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
23    * Shell command directly uses variable SCRIPTS from outside of the rule:
24      It is recommended to pass all files as input and output, and non-file
25      parameters via the params directive. Otherwise, provenance tracking is
26      less accurate.
27      Also see:
28      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
Formatting results
1[DEBUG] 
2[DEBUG] In file "/tmp/tmpcyj6aeol/ausswal-PromEvo-3854b36/workflow/Snakefile":  Formatted content is different from original
3[INFO] 1 file(s) would be changed 😬
4
5snakefmt version: 0.11.5