solida-core/musta

MUSTA: a modular pipeline to detect, classify and interpret mutations in cancer

Overview

Topics:

Latest release: v1.2.2, Last update: 2024-07-05

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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/solida-core/musta . --tag v1.2.2

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.

General settings

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

Sample sheet

Add samples to config/samples.yaml. For each sample, specify a normal_bam [optional] and one (or more SOON) tumor_bam.

  • If normal_bam is not provided, the pipeline performs a tumor-only analysis.
  • You don't have to rename your bam files, the pipeline detect the correct sample name from the BAM header.
  • You can specify manually the sample name in the normal_sample_name and tumor_sample_name fields.

Other input files can be defined for each sample, depending on the analysis starting point:

  • vcf: the analysis will start with the annotation of vcf file with Funcotator
  • maf: input maf files will be processed with the generation of summary plots and tables for analysis results interpretation.

An example of sample record in config/config.yaml is reported below:

patientA:
  normal_sample_name:
    - normalname
  tumor_sample_name:
    - tumorname
  normal_bam:
    - path/to/normal.bam
  tumor_bam:
    - path/to/tumor.bam
  vcf:
    - path/to/tumor_normal.vcf.gz
  maf:
    - path/to/sample.maf

Remember to delete the empty sections in a sample, i.e. if you have only a maf file, the sample record would look like:

patientA:
  maf:
    - path/to/sample.maf

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