richardstoeckl/QCforSeqCode

Snakemake Pipeline to check the requirements for a prokaryotic assembly to be included in the SeqCode initiative

Overview

Topics:

Latest release: v1.0.2, Last update: 2024-12-05

Linting: linting: failed, Formatting:formatting: failed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

mamba create -c conda-forge -c bioconda --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

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/richardstoeckl/QCforSeqCode . --tag v1.0.2

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.

Usage and configuration

Here is a rough overview:

  1. Install conda (mamba or miniconda is fine).
  2. Install snakemake with:
conda install -c conda-forge -c bioconda snakemake
  1. Download checkm2 database (via wget https://zenodo.org/api/files/fd3bc532-cd84-4907-b078-2e05a1e46803/checkm2_database.tar.gz)
  2. Download GTDB-Tk database (via wget https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_package/full_package/gtdbtk_r220_data.tar.gz)
  3. Download the latest release from this repo and cd into it
  4. Edit the config/config.yaml to provide the paths to your results/logs directories, and the paths to the databases you downloaded, as well as any parameters you might want to change.
  5. Edit the config/sampleData.csv file with the specific details for each assembly you want to check. Depending on what you enter here, the pipeline will automatically adjust what will be done.

General configuration

To configure this workflow, modify config/config.yaml according to your needs, following the explanations provided in the file.

"Main" section

Here you should provide the paths to your intermediary/results/logs directories. The interim directory will contain larger intermediary files. The results directory will contain the final output of the pipeline. The logdirectory will be used to store the log files for each step. Here you should also write the name of your sample data file (see relevant section below).

"Tools" section

Here you should give the paths to the databases needed for some of the tools.

sampleData file setup

The setup of the samples is specified via comma-separated values files (.csv). You can use the config/sampleData.csvfile as a template.

Linting and formatting

Linting results

Lints for snakefile /tmp/tmp5esooj2s/richardstoeckl-QCforSeqCode-68d7f93/workflow/Snakefile:
    * Mixed rules and functions in same snakefile.:
      Small one-liner functions used only once should be defined as lambda
      expressions. Other functions should be collected in a common module, e.g.
      'rules/common.smk'. This makes the workflow steps more readable.
      Also see:
      https://snakemake.readthedocs.io/en/latest/snakefiles/modularization.html#includes

Lints for rule seqkit_stats (line 40, /tmp/tmp5esooj2s/richardstoeckl-QCforSeqCode-68d7f93/workflow/Snakefile):
    * No log directive defined:
      Without a log directive, all output will be printed to the terminal. In
      distributed environments, this means that errors are harder to discover.
      In local environments, output of concurrent jobs will be mixed and become
      unreadable.
      Also see:
      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files

Lints for rule collectAssemblies (line 57, /tmp/tmp5esooj2s/richardstoeckl-QCforSeqCode-68d7f93/workflow/Snakefile):
    * No log directive defined:
      Without a log directive, all output will be printed to the terminal. In

... (truncated)

Formatting results

[DEBUG] 
[DEBUG] In file "/tmp/tmp5esooj2s/richardstoeckl-QCforSeqCode-68d7f93/workflow/Snakefile":  Formatted content is different from original
[INFO] 1 file(s) would be changed 😬

snakefmt version: 0.10.2