Speeding Up FASTQ Preprocessing with FastP

In next-generation sequencing (NGS) workflows, clean data is critical. Low-quality reads, adapter sequences, and other artifacts can heavily impact downstream analyses like genome assembly, transcript quantification, or variant calling. FastP is a modern, ultra-efficient tool that performs both quality control and read cleaning — all in a single fast pass through your FASTQ files. Let's dive into how you can install and use FastP for your omics pipelines!

What is FastP?

FastP is an all-in-one FASTQ preprocessing tool written in C++ designed for maximum speed and minimal memory usage. Whether you're trimming adapters, filtering poor-quality reads, or visualizing sequencing quality, FastP handles it all — and does it very quickly.

FastP highlights:

• Adapter trimming (automatic detection for paired-end reads)
• Base quality filtering (Phred score based)
• Length filtering (minimum read length enforcement)
• Low complexity read filtering
• Overlapping paired-end read correction
• Comprehensive quality reports (HTML + JSON)

Why Choose FastP?

• 🚀 Lightning speed: Preprocess large FASTQ files in minutes, not hours.
• 🛠 Built-in quality control: Get clean data and QC reports without extra tools.
• 🧹 All-in-one solution: No need to chain multiple tools like cutadapt + Trimmomatic + FastQC.
• ☁️ Cloud ready: Easily containerized with Docker for scalable workflows.
• 🛠 Frequent updates: Actively maintained by the community.

Installing FastP

🐍 Conda Local Installation

1. Install FastP using conda:

   conda install -c bioconda fastp=0.24.1
   

   or, if you prefer mamba:

   mamba install -c bioconda fastp=0.24.1
   

2. Verify the installation:

   fastp --version
   

   This should return the version number, e.g., fastp 0.24.1.

   Notes: If you are using a conda environment, make sure to activate it first. Also, if you are using a different version of FastP, adjust the version number accordingly.

🐳 Docker Local Installation

This installs FastP v0.24.1 inside a lightweight container — perfect for local or cloud workflows. To build the Docker image:

1. Create a Dockerfile.fastp in your working directory.

   Example Dockerfile for FastP

   FROM mambaorg/micromamba:2.0-debian11
   
   RUN micromamba install -c bioconda -c conda-forge fastp==0.24.1 \
       && micromamba clean -a -y
   

2. Build the Docker image:

   docker build \
     -f Dockerfile.fastp \
     -t fastp:0.24.1 .
   

3. Verify the installation:

   docker run --rm fastp:0.24.1 fastp --version
   

   This should return the version number, e.g., fastp 0.24.1.

   Notes: If you are using a different version of FastP, adjust the version number accordingly.

☁️ Cloud Deployment (Google Cloud Platform)

1. Build the image locally as shown above.

2. Tag it for GCP Artifact Registry:

   docker tag fastp:0.24.1 us-central1-docker.pkg.dev/my-project-id/my-repo/fastp:0.24.1
   

3. Push it to the Artifact Registry:

   docker push us-central1-docker.pkg.dev/my-project-id/my-repo/fastp:0.24.1
   

   Ensure you have gcloud CLI installed and configured for authentication.

Running FastP on Sample Data

📥 Download Test FASTQ Files

Let's grab a paired-end dataset for Bacillus subtilis ALBA01 strain from the European Nucleotide Archive:

# Make data directory if it doesn't exist,
mkdir -p data
# Download FASTQ files for Bacillus subtilis ALBA01
wget -nc -P ./data ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR331/005/SRR3317165/SRR3317165_1.fastq.gz
wget -nc -P ./data ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR331/005/SRR3317165/SRR3317165_2.fastq.gz

🚀 Process with FastP (Docker)

Now let's run preprocessing:

docker run --rm -it \
  -v "$(pwd):/app" \
  --user 1000:1000 \
  fastp:0.24.1 \
  bash -c '
    fastp \
      --in1 "/app/data/SRR3317165_1.fastq.gz" \
      --in2 "/app/data/SRR3317165_2.fastq.gz" \
      --out1 "/app/data/SRR3317165_1.trim.fastq.gz" \
      --out2 "/app/data/SRR3317165_2.trim.fastq.gz" \
      --unpaired1 "/app/data/SRR3317165_1.trim_up.fastq.gz" \
      --unpaired2 "/app/data/SRR3317165_2.trim_up.fastq.gz" \
      --qualified_quality_phred 20 \
      --detect_adapter_for_pe \
      --length_required 50 \
      --correction \
      --low_complexity_filter \
      --complexity_threshold 30 \
      --html /app/data/fastp.html \
      --json /app/data/fastp.json \
      --thread 16'

📂 Output Files

FastP will generate:

📊 Quality Report

Key FastP Parameters You Should Know

FastP in Your Omics Workflow

A standard workflow incorporating FastP would look like this:

1. Raw Data QC: Run FastQC-RS to assess unprocessed reads.
2. Preprocessing: Run FastP to trim adapters, filter reads, and generate clean datasets.
3. Post-QC: Run FastQC-RS again to confirm data quality improvements.
4. Analysis: Proceed to alignment, assembly, or quantification steps.

🎯 Conclusion

FastP is an amazing tool for sequencing data preprocessing — ultra-fast, user-friendly, and packed with features. If you are building NGS workflows for genomics, transcriptomics, or metagenomics projects, FastP is the perfect starting point for producing high-quality, reliable datasets.

Clean data leads to better science!

📚 References

• FastP GitHub
• FastP Documentation
• European Nucleotide Archive (ENA)
• Docker Documentation
• Google Cloud SDK
• SeqContainerLab GitHub