sanjaysgk/WES_RNA_Pipeline

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Overview

Latest release: None, Last update: 2025-09-09

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Wrappers: bio/fastqc bio/multiqc

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda. 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/sanjaysgk/WES_RNA_Pipeline . --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

Parameters

This table lists all parameters that can be used to run the workflow.

parameter	type	details	default
samplesheet
path	str	path to samplesheet, mandatory	“config/samples.tsv”
get_genome
ncbi_ftp	str	link to a genome on NCBI’s FTP server	link to S. cerevisiae genome
simulate_reads
read_length	num	length of target reads in bp	100
read_number	num	number of total reads to be simulated	10000
Got it 👍 — we’ll adapt this README/workflow overview to your WES Mutation Calling Pipeline instead of the toy demo (genome download, dwgsim, etc.).

Here’s a rewritten version that reflects your pipeline steps (Tumor/Normal WES, alignment, QC, variant calling, etc.):

---

WES Mutation Calling Pipeline

This workflow is a whole exome sequencing (WES) best-practice pipeline for detecting somatic variants from paired tumor/normal samples. The workflow is built using Snakemake and consists of the following steps:

1. Prepare references

   • Validate and index the reference genome (FASTA, dict, fai, BWA index)

   • Index known variant databases (dbSNP, etc.)

2. Quality control of raw data

   • Run FastQC on raw FASTQ reads

   • Summarize results with MultiQC

3. Alignment and BAM processing

   • Align tumor and normal samples with BWA-MEM2

   • Sort, index, and mark duplicates with GATK

   • Collect alignment QC metrics

4. Base quality score recalibration (BQSR)

   • Recalibrate BAMs using known sites

   • Apply recalibration

5. Somatic variant calling

   • Run GATK Mutect2 on paired tumor/normal

   • Filter calls and create final VCF

6. Variant QC & reporting

   • Generate summary statistics

   • Produce MultiQC report of all steps

---

Running the workflow

Input data

The workflow expects paired-end FASTQ files for both tumor and normal samples. The input samples are described in a mandatory table defined in config/samples.tsv.

Example layout:

sample_id	type	read1	read2
ORG125	tumor	ORG125T_Data_L1_1.fq.gz	ORG125T_Data_L1_2.fq.gz
ORG125	normal	ORG125N_Data_L1_1.fq.gz	ORG125N_Data_L1_2.fq.gz

Parameters

This table lists all configurable parameters.

parameter	type	details	default
samplesheet	str	path to sample sheet, mandatory	config/samples.tsv
ref_genome	str	path to reference FASTA	references/GRCh38.fa
known_sites	str	dbSNP / known indels VCF	references/dbsnp.hg38.vcf.gz
threads	int	number of CPU threads per job	16
memory	str	memory per process	19G

---

Example run

Dry run (see planned steps):

snakemake -n --use-conda

Run on 16 cores with conda:

snakemake --cores 16 --use-conda

Run on SLURM cluster:

snakemake --profile slurm

---

🔥 This replaces the toy workflow with a real WES mutation calling pipeline while keeping the same structure and readability.

Do you want me to also rewrite the Snakefile rules (alignment, BQSR, Mutect2, etc.) to match your bash pipeline? That way you’d have a fully automated Snakemake version of your script.

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