sanjaysgk/WES_RNA_Pipeline

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Overview

Latest release: None, Last update: 2025-09-25

Linting: linting: failed, Formatting: formatting: failed

Wrappers: bio/bwa-mem2/mem 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.

Linting and formatting

Linting results

 1Workflow defines that rule get_genome is eligible for caching between workflows (use the --cache argument to enable this).
 2Workflow defines that rule genome_faidx is eligible for caching between workflows (use the --cache argument to enable this).
 3Workflow defines that rule genome_dict is eligible for caching between workflows (use the --cache argument to enable this).
 4Workflow defines that rule bwa_index is eligible for caching between workflows (use the --cache argument to enable this).
 5Lints for rule index_reference (line 24, /tmp/tmp27j8xxqz/workflow/rules/ref.smk):
 6    * No log directive defined:
 7      Without a log directive, all output will be printed to the terminal. In
 8      distributed environments, this means that errors are harder to discover.
 9      In local environments, output of concurrent jobs will be mixed and become
10      unreadable.
11      Also see:
12      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
13    * Specify a conda environment or container for each rule.:
14      This way, the used software for each specific step is documented, and the
15      workflow can be executed on any machine without prerequisites.
16      Also see:
17      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
18      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers

Formatting results

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