Sequana FastQC
sequana_fastqc is a Sequana pipeline for quality control of raw sequencing data. It runs FastQC and MultiQC on FASTQ files to assess sequencing quality, identify technical issues, and detect common contaminants.
This page hosts example HTML outputs generated by sequana_fastqc on real datasets so you can explore the reports without running the pipeline yourself.
Overview
The pipeline performs the following steps:
- Per-sample FastQC — per-base quality scores, sequence composition, adapter contamination
- Quality aggregation — MultiQC report summarizing results across all samples
- Outlier detection — automated flagging of samples with quality issues
Use this pipeline early in your analysis workflow to ensure data quality before proceeding with downstream processing (mapping, variant calling, assembly, etc.).
Demo Dataset
The reports below were produced from paired-end RNA-seq reads of Measles virus (archival test data). The dataset is publicly available on Zenodo (record 12187421) and consists of 2 paired-end FASTQ files (R1 and R2) totaling ~143 MB when compressed.
Show command line & data links
sequana_fastqc \ --input-directory measles/ \ --working-directory fastqc_report \ --force
Test data (Zenodo 12187421):
measles_R1_.fastq.gz · measles_R2_.fastq.gz
Versions: sequana_fastqc 0.1.x · sequana 0.20.x · FastQC 0.11.x · MultiQC 1.13+
HTML Report Examples
⚙️ How to Use This Pipeline
Install sequana_fastqc and run on your own data:
1. Install
pip install sequana_fastqc
2. Initialize workflow
sequana_fastqc --input-directory /path/to/fastqs --working-directory fastqc_output
2b. (Optional) Use Apptainer for containerized dependencies
If you prefer isolated environments, Apptainer automatically downloads and caches container images of FastQC, MultiQC, and other dependencies:
sequana_fastqc --input-directory /path/to/fastqs \ --working-directory fastqc_output \ --apptainer-prefix ~/images
The --apptainer-prefix ~/images flag tells Snakemake to download and cache third-party tool containers (FastQC, MultiQC) in ~/images/ for reuse across runs.
3. Run with Snakemake
cd fastqc_output snakemake -c 4 # Use as many cores as needed
For more options, see the GitHub README or Sequana documentation.