Snakemake workflow: FastQC and MultiQC of Next-generation Sequencing Data

Motivation

This is a Snakemake pipeline for quality control of Illumina next-generation sequencing data. It performs quality check using FastQC on raw fastq-files and merges fastqc reports using MultiQC.

Prerequisites

• Conda is a package, dependency and environment management system that is used to install software packages and manage their dependencies. It runs on Linux, OS X and Windows, and was created for Python programs but it can package and distribute software for any language. install conda for your operating system: Linux, MacOS.

• Snakemake is a workflow management system that allows to create reproducible and scalable data analyses.

• FastQC is a quality control tool for high throughput sequence data.

• MultiQC is a tool to aggregate bioinformatics results across many samples into a single report. Configuration The configuration file is located in config/config.yaml. This file contains paths to input files and directories, output directories, and other settings.

Usage

1. Clone the repository:

git clone https://github.com/kevin-wamae/fastqc-multiqc-pipeline.git

2. Navigate into the cloned directory using the following command:

cd fastqc-multiqc-pipeline

3. Create a conda environment (named fastqc-multiqc-pipeline) for the pipeline:

conda env create --file workflow/envs/environment.yaml

1. Activate the conda environment. This needs to be done every time you exit and restart your terminal and want re-run this pipeline:

conda activate fastqc-multiqc-pipeline

1. Run the pipeline with Snakemake:

snakemake --cores <number_of_cores> --use-conda

• The --cores option specifies the number of cores to use, and the --use-conda option tells Snakemake to use the specified conda environments.

6. Finally, the config file is located in config/config.yaml.
   • This file contains paths to input files and directories, output directories, and other settings such as the number of cores to use.
   • You can edit this file to suit your needs.
   • For example, you can change the number of cores to use by editing the extra:threads: parameter

Output

• The pipeline generates HTML reports for each sample in the fastqc directory and a merged HTML report in the multiqc directory.

Dependencies

• This pipeline uses conda environments to manage dependencies for each rule. The environments are defined in envs/fastqc.yaml and envs/multiqc.yaml.

Contact

• Report any issues or bugs by openning an issue here or contact me via email (wamaekevin[at]gmail.com)